0. Beginning stuff

library(multcomp)
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library(psych)
library(tidyverse)
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## cjoint: AMCE Estimator for Conjoint Experiments
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## Authors: Soubhik Barari, Elissa Berwick, Jens Hainmueller, Daniel Hopkins, Sean Liu, Anton Strezhnev, Teppei Yamamoto
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source("http://rfs.kvasaheim.com/stat200.R") # importing functions
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## Happy analyzing!!!
library(readr)
library(Hmisc)
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library("effects")
library(forcats)
options(scipen = 100)
wf_dt <- read_csv("~/Documents/White Fragility/Buying Black and Moral Affirmation/wf_maS1.csv")
## Rows: 327 Columns: 116
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (39): StartDate, EndDate, RecordedDate, ResponseId, DistributionChannel,...
## dbl (76): ParticipantID, Status, Progress, Duration (in seconds), Finished, ...
## num  (1): race
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## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
View(wf_dt)
attach(wf_dt)
colnames(wf_dt)
##   [1] "ParticipantID"         "StartDate"             "EndDate"              
##   [4] "Status"                "Progress"              "Duration (in seconds)"
##   [7] "Finished"              "RecordedDate"          "ResponseId"           
##  [10] "DistributionChannel"   "UserLanguage"          "consent"              
##  [13] "prolificID"            "BA_1"                  "BA_2"                 
##  [16] "BA_3"                  "BA_4"                  "SE1"                  
##  [19] "BMIS_sad_1"            "BMIS_shame_1"          "BMIS_guilt_1"         
##  [22] "BMIS_tired_1"          "BMIS_nervous_1"        "BMIS_calm_1"          
##  [25] "BMIS_fedup_1"          "BMIS_loving_1"         "BMIS_angry_1"         
##  [28] "BMIS_lively_1"         "BMIS_caring_1"         "BMIS_content_1"       
##  [31] "BMIS_gloomy_1"         "BMIS_jittery_1"        "BMIS_drowsy_1"        
##  [34] "BMIS_happy_1"          "Q1 RP1"                "Q2 RP2"               
##  [37] "Q3 RP3"                "Q4 RP4"                "Q5 RP5"               
##  [40] "Q6 RP6"                "Q7 RP7"                "Q8 RN1"               
##  [43] "Q9 RN2"                "Q10 RN3"               "Q11 RN4"              
##  [46] "Q12 RN5"               "Q13 RN6"               "Q14 RN7"              
##  [49] "Q15 LP1"               "Q16 LP2"               "Q17 LP3"              
##  [52] "Q18 LP4"               "Q19 LP5"               "Q20 LP6"              
##  [55] "Q21 LP7"               "Q22 LN1"               "Q23 LN2"              
##  [58] "Q24 LN3"               "Q25 LN4"               "Q26 LN5"              
##  [61] "Q27 LN6"               "Q28 LN7"               "spwtime_First Click"  
##  [64] "spwtime_Last Click"    "spwtime_Page Submit"   "spwtime_Click Count"  
##  [67] "attentioncheck_nb"     "discrepancy_nb"        "BMIS_sad_2"           
##  [70] "BMIS_shame_2"          "BMIS_guilt_2"          "BMIS_tired_2"         
##  [73] "BMIS_nervous_2"        "BMIS_calm_2"           "BMIS_fedup_2"         
##  [76] "BMIS_loving_2"         "BMIS_angry_2"          "BMIS_lively_2"        
##  [79] "BMIS_caring_2"         "BMIS_content_2"        "BMIS_gloomy_2"        
##  [82] "BMIS_jittery_2"        "BMIS_drowsy_2"         "BMIS_happy_2"         
##  [85] "credibility"           "objective"             "valid"                
##  [88] "useful"                "rl_product_choice"     "rl_shop_intentions"   
##  [91] "rl_purchase"           "rl_wom"                "rr_product_choice"    
##  [94] "rr_shop_intentions"    "rr_purchase"           "rr_wom"               
##  [97] "attentioncheck_b"      "discrepancy_b"         "SE_2"                 
## [100] "age"                   "race"                  "education"            
## [103] "polit_affil"           "polit_affil_4_TEXT"    "polit_affil_cont_1"   
## [106] "gender"                "gender_4_TEXT"         "iat_prev"             
## [109] "iat_racial"            "iat_racial_time"       "iat_racial_quant"     
## [112] "recent_results"        "nobias_white"          "nobias_black"         
## [115] "bias_white"            "bias_black"
summary(wf_dt[,19:34])
##    BMIS_sad_1     BMIS_shame_1    BMIS_guilt_1    BMIS_tired_1  BMIS_nervous_1 
##  Min.   :1.000   Min.   :1.000   Min.   :1.000   Min.   :1.00   Min.   :1.000  
##  1st Qu.:1.000   1st Qu.:1.000   1st Qu.:1.000   1st Qu.:2.00   1st Qu.:1.000  
##  Median :2.000   Median :1.000   Median :1.000   Median :3.00   Median :2.000  
##  Mean   :1.923   Mean   :1.611   Mean   :1.675   Mean   :2.71   Mean   :1.871  
##  3rd Qu.:3.000   3rd Qu.:2.000   3rd Qu.:2.000   3rd Qu.:3.00   3rd Qu.:3.000  
##  Max.   :4.000   Max.   :4.000   Max.   :4.000   Max.   :4.00   Max.   :4.000  
##  NA's   :16      NA's   :16      NA's   :16      NA's   :17     NA's   :16     
##   BMIS_calm_1     BMIS_fedup_1  BMIS_loving_1    BMIS_angry_1   BMIS_lively_1  
##  Min.   :1.000   Min.   :1.00   Min.   :1.000   Min.   :1.000   Min.   :1.000  
##  1st Qu.:3.000   1st Qu.:1.00   1st Qu.:2.000   1st Qu.:1.000   1st Qu.:2.000  
##  Median :3.000   Median :1.00   Median :3.000   Median :1.000   Median :2.000  
##  Mean   :3.132   Mean   :1.81   Mean   :2.728   Mean   :1.437   Mean   :2.259  
##  3rd Qu.:4.000   3rd Qu.:3.00   3rd Qu.:3.000   3rd Qu.:2.000   3rd Qu.:3.000  
##  Max.   :4.000   Max.   :4.00   Max.   :4.000   Max.   :4.000   Max.   :4.000  
##  NA's   :16      NA's   :16     NA's   :18      NA's   :16      NA's   :18     
##  BMIS_caring_1   BMIS_content_1  BMIS_gloomy_1   BMIS_jittery_1 
##  Min.   :1.000   Min.   :1.000   Min.   :1.000   Min.   :1.000  
##  1st Qu.:2.000   1st Qu.:2.000   1st Qu.:1.000   1st Qu.:1.000  
##  Median :3.000   Median :3.000   Median :2.000   Median :1.000  
##  Mean   :2.929   Mean   :2.842   Mean   :1.897   Mean   :1.605  
##  3rd Qu.:4.000   3rd Qu.:4.000   3rd Qu.:3.000   3rd Qu.:2.000  
##  Max.   :4.000   Max.   :4.000   Max.   :4.000   Max.   :4.000  
##  NA's   :16      NA's   :16      NA's   :16      NA's   :16     
##  BMIS_drowsy_1    BMIS_happy_1  
##  Min.   :1.000   Min.   :1.000  
##  1st Qu.:1.000   1st Qu.:2.000  
##  Median :2.000   Median :3.000  
##  Mean   :2.129   Mean   :2.768  
##  3rd Qu.:3.000   3rd Qu.:3.000  
##  Max.   :4.000   Max.   :4.000  
##  NA's   :17      NA's   :16
summary(wf_dt[,69:84])
##    BMIS_sad_2     BMIS_shame_2    BMIS_guilt_2    BMIS_tired_2  
##  Min.   :1.000   Min.   :1.000   Min.   :1.000   Min.   :1.000  
##  1st Qu.:1.000   1st Qu.:1.000   1st Qu.:1.000   1st Qu.:2.000  
##  Median :2.000   Median :1.000   Median :1.000   Median :3.000  
##  Mean   :1.986   Mean   :1.776   Mean   :1.763   Mean   :2.642  
##  3rd Qu.:3.000   3rd Qu.:3.000   3rd Qu.:3.000   3rd Qu.:3.000  
##  Max.   :4.000   Max.   :4.000   Max.   :4.000   Max.   :4.000  
##  NA's   :32      NA's   :33      NA's   :32      NA's   :34     
##  BMIS_nervous_2   BMIS_calm_2     BMIS_fedup_2   BMIS_loving_2  
##  Min.   :1.000   Min.   :1.000   Min.   :1.000   Min.   :1.000  
##  1st Qu.:1.000   1st Qu.:3.000   1st Qu.:1.000   1st Qu.:2.000  
##  Median :2.000   Median :3.000   Median :1.000   Median :3.000  
##  Mean   :1.844   Mean   :2.963   Mean   :1.687   Mean   :2.693  
##  3rd Qu.:3.000   3rd Qu.:4.000   3rd Qu.:2.000   3rd Qu.:3.000  
##  Max.   :4.000   Max.   :4.000   Max.   :4.000   Max.   :4.000  
##  NA's   :33      NA's   :32      NA's   :33      NA's   :34     
##   BMIS_angry_2   BMIS_lively_2   BMIS_caring_2   BMIS_content_2 
##  Min.   :1.000   Min.   :1.000   Min.   :1.000   Min.   :1.000  
##  1st Qu.:1.000   1st Qu.:2.000   1st Qu.:2.000   1st Qu.:2.000  
##  Median :1.000   Median :2.000   Median :3.000   Median :3.000  
##  Mean   :1.485   Mean   :2.278   Mean   :2.827   Mean   :2.803  
##  3rd Qu.:2.000   3rd Qu.:3.000   3rd Qu.:4.000   3rd Qu.:3.500  
##  Max.   :4.000   Max.   :4.000   Max.   :4.000   Max.   :4.000  
##  NA's   :32      NA's   :32      NA's   :33      NA's   :32     
##  BMIS_gloomy_2   BMIS_jittery_2  BMIS_drowsy_2    BMIS_happy_2  
##  Min.   :1.000   Min.   :1.000   Min.   :1.000   Min.   :1.000  
##  1st Qu.:1.000   1st Qu.:1.000   1st Qu.:1.000   1st Qu.:2.000  
##  Median :2.000   Median :1.000   Median :2.000   Median :3.000  
##  Mean   :1.898   Mean   :1.646   Mean   :2.156   Mean   :2.617  
##  3rd Qu.:3.000   3rd Qu.:2.000   3rd Qu.:3.000   3rd Qu.:3.000  
##  Max.   :4.000   Max.   :4.000   Max.   :4.000   Max.   :4.000  
##  NA's   :33      NA's   :33      NA's   :32      NA's   :32
summary(wf_dt[,14:17])
##       BA_1            BA_2            BA_3           BA_4      
##  Min.   :1.000   Min.   :1.000   Min.   :1.00   Min.   :1.000  
##  1st Qu.:2.000   1st Qu.:2.000   1st Qu.:2.00   1st Qu.:2.000  
##  Median :3.000   Median :3.000   Median :4.00   Median :4.000  
##  Mean   :3.334   Mean   :3.273   Mean   :3.73   Mean   :3.987  
##  3rd Qu.:5.000   3rd Qu.:5.000   3rd Qu.:5.00   3rd Qu.:5.500  
##  Max.   :7.000   Max.   :7.000   Max.   :7.00   Max.   :7.000  
##  NA's   :16      NA's   :16      NA's   :16     NA's   :16
wf_dt$discrepancy_nb <- car::recode(wf_dt$discrepancy_nb, "1 = '3'; 2 = '2'; 3 = '1'; 4 = '0'; 5 = '-1'; 6 = '-2'; 7 = '-3'")
wf_dt$attentioncheck_nb <- car::recode(wf_dt$attentioncheck_nb, "1 = '3'; 2 = '2'; 3 = '1'; 4 = '0'; 5 = '-1'; 6 = '-2'; 7 = '-3'")
wf_dt$discrepancy_b <- car::recode(wf_dt$discrepancy_b, "1 = '3'; 2 = '2'; 3 = '1'; 4 = '0'; 5 = '-1'; 6 = '-2'; 7 = '-3'")
wf_dt$attentioncheck_b <- car::recode(wf_dt$attentioncheck_b, "1 = '3'; 2 = '2'; 3 = '1'; 4 = '0'; 5 = '-1'; 6 = '-2'; 7 = '-3'") # larger scores indicate greater changes

wf_dt$discrepancy_bias <- (wf_dt$discrepancy_b - wf_dt$attentioncheck_b) # deal with this later
wf_dt$discrepancy_nobias <- (wf_dt$discrepancy_nb + wf_dt$discrepancy_nb) # deal with this later
wf_dt$bias_discrepancy <- coalesce(wf_dt$discrepancy_bias, wf_dt$discrepancy_nobias) 
wf_dt$attentioncheck_nb <- as.numeric(wf_dt$attentioncheck_nb)
is.numeric(wf_dt$attentioncheck_nb)
## [1] TRUE
which(wf_dt$attentioncheck_nb == "1", arr.ind = TRUE) # seeing which people failed my attention checks
## [1]  36 116 179 212 277 283 310 318 319
which(wf_dt$attentioncheck_nb == "2", arr.ind = TRUE)
## [1]  77 115 208 240 247 249
which(wf_dt$attentioncheck_nb == "3", arr.ind = TRUE)
## [1]  21 167 192 325
which(wf_dt$attentioncheck_nb == "5", arr.ind = TRUE)
## integer(0)
which(wf_dt$attentioncheck_nb == "6", arr.ind = TRUE)
## integer(0)
which(wf_dt$attentioncheck_nb == "7", arr.ind = TRUE)
## integer(0)
wf_dt$attentioncheck_b <- as.numeric(wf_dt$attentioncheck_b)
is.numeric(wf_dt$attentioncheck_b)
## [1] TRUE
which(wf_dt$attentioncheck_b == "4", arr.ind = TRUE) # seeing which people failed my attention checks
## integer(0)
which(wf_dt$attentioncheck_b == "2", arr.ind = TRUE)
## [1]   2  26  69 134 200 201 251 261 272
which(wf_dt$attentioncheck_b == "3", arr.ind = TRUE)
##   [1]   5   9  11  17  20  24  29  30  34  35  37  38  40  43  44  45  48  49
##  [19]  50  52  56  58  62  63  64  73  75  80  83  86  87  91  92  93  94  97
##  [37]  98 102 104 105 107 108 112 122 124 125 128 129 131 135 136 137 139 140
##  [55] 143 144 146 147 149 150 151 154 157 160 161 163 164 176 180 182 183 184
##  [73] 185 186 187 188 189 190 193 195 196 198 202 205 226 231 232 233 236 238
##  [91] 239 245 248 252 253 255 257 258 259 263 266 268 270 273 275 276 278 279
## [109] 281 282 285 286 289 293 294 296 297 300 304 305 306 311 312 313 316 317
## [127] 320 321 322 324 326 327
which(wf_dt$attentioncheck_b == "5", arr.ind = TRUE)
## integer(0)
which(wf_dt$attentioncheck_b == "6", arr.ind = TRUE)
## integer(0)
which(wf_dt$attentioncheck_b == "7", arr.ind = TRUE)
## integer(0)
wf_dt2 <- wf_dt[-c(61, 81, 88, 82, 15, 301, 315, 323, 254),] # only lost 9 people
wf_dt2$bias_discrepancy_centered <- scale(wf_dt2$bias_discrepancy, center = TRUE, scale = FALSE) # Centering
wf_dt2$bias_discrepancy_Z <- scale(wf_dt2$bias_discrepancy, center = TRUE, scale = TRUE) # Z-score
wf_dt2 <- wf_dt2 %>%
  mutate(
    condition = rowSums(select(., nobias_white, nobias_black, bias_white, bias_black), na.rm = TRUE)
  )
as.factor(wf_dt2$condition)
##   [1] 0 3 0 1 2 0 1 0 2 1 3 1 0 1 0 3 1 0 2 1 1 0 3 1 2 0 1 3 3 0 1 3 2 3 0 3 2
##  [38] 0 3 1 0 2 2 3 0 0 2 2 2 0 3 0 1 1 3 1 3 3 0 2 3 2 0 1 0 1 2 0 2 0 2 0 3 1
##  [75] 1 3 1 2 2 0 0 2 3 2 0 2 3 3 3 1 1 3 2 1 1 0 3 0 3 3 1 3 3 0 0 1 3 0 0 1 1
## [112] 0 2 1 0 2 2 0 2 3 0 1 3 3 0 3 1 0 2 3 2 2 1 2 2 1 1 2 2 0 2 3 0 3 2 2 1 0
## [149] 3 0 3 3 1 0 3 2 0 2 2 0 1 0 1 1 0 1 0 0 1 1 3 1 0 0 2 1 3 2 2 2 2 3 2 3 3
## [186] 0 1 3 1 3 2 0 3 0 3 2 2 3 2 3 0 3 1 1 0 0 1 0 0 0 0 3 1 1 2 1 0 0 3 2 2 2
## [223] 1 0 0 2 3 2 0 1 3 1 3 2 0 0 1 2 0 3 0 1 2 1 0 2 3 3 2 1 3 2 3 0 2 1 3 0 1
## [260] 3 0 2 1 2 0 3 2 1 3 2 0 2 3 1 2 3 0 1 2 3 1 0 2 1 0 0 2 3 0 3 2 0 1 2 0 0
## [297] 2 3 3 0 1 0 1 3 3 2 1 2 3 1 1 2 2 3 2 1 2 3
## Levels: 0 1 2 3
wf_dt2 <- wf_dt2 %>%
  mutate(
    bias = case_when(
      condition %in% c(0, 1) ~ 0,
      condition %in% c(2, 3) ~ 1,
      TRUE ~ NA_integer_
    ),
    brand_race = case_when(
      condition %in% c(0, 2) ~ 0,
      condition %in% c(1, 3) ~ 1,
      TRUE ~ NA_integer_
    )
  )
wf_dt2 <- wf_dt2 %>%
  mutate(
    shop_intentions = rowSums(select(., rl_shop_intentions, rr_shop_intentions), na.rm = TRUE)
  )
# Centering and z-score for "shop_intentions"
wf_dt2$shop_intentions_centered <- scale(wf_dt2$shop_intentions, center = TRUE, scale = FALSE) # Centering
wf_dt2$shop_intentions_Z <- scale(wf_dt2$shop_intentions, center = TRUE, scale = TRUE) # Z-score
wf_dt2 <- wf_dt2 %>%
  mutate(
    purchase = rowSums(select(., rl_purchase, rr_purchase), na.rm = TRUE)
  )
# Centering and z-score for "purchase"
wf_dt2$purchase_centered <- scale(wf_dt2$purchase, center = TRUE, scale = FALSE) # Centering
wf_dt2$purchase_Z <- scale(wf_dt2$purchase, center = TRUE, scale = TRUE) # Z-score
wf_dt2 <- wf_dt2 %>%
  mutate(
    wom = rowSums(select(., rl_wom, rr_wom), na.rm = TRUE)
  )
# Centering and z-score for "wom"
wf_dt2$wom_centered <- scale(wf_dt2$wom, center = TRUE, scale = FALSE) # Centering
wf_dt2$wom_Z <- scale(wf_dt2$wom, center = TRUE, scale = TRUE) # Z-score

** Demographics**

race_proportions <- table(wf_dt2$race)/length(wf_dt2$race) # creating race table
(race_percentages <- race_proportions*100) # multiplying the table by 100 to get percentages
## 
##          1          3          4          6          7         14         15 
## 86.7924528  0.3144654  0.3144654  0.3144654  0.6289308  0.3144654  0.3144654 
##         16 
##  0.9433962
gender_proportions <- table(wf_dt2$gender)/length(wf_dt2$gender) # creating gender table
(gender_percentages <- gender_proportions*100) # multiplying the table by 100 to get percentages
## 
##          1          2          3          4          5 
## 41.8238994 45.2830189  2.2012579  0.3144654  0.3144654
political_proportions <- table(wf_dt2$polit_affil)/length(wf_dt2$polit_affil) # creating political table
(political_percentages <- political_proportions*100) # multiplying the table by 100 to get percentages
## 
##         1         2         3         4         5 
## 17.610063 47.169811 22.012579  1.886792  1.257862
education_proportions <- table(wf_dt2$education)/length(wf_dt2$education) # creating education table
(education_percentages <- education_proportions*100) # multiplying the table by 100 to get percentages
## 
##          1          2          3          4          5          6          7 
##  0.6289308 10.6918239 18.8679245  9.7484277 36.4779874 12.8930818  0.6289308
iatprev_proportions <- table(wf_dt2$iat_prev)/length(wf_dt2$iat_prev) # creating IAT previously table
(iatprev_percentages <- iatprev_proportions*100) # multiplying the table by 100 to get percentages
## 
##       21       22 
## 59.74843 30.18868
iatracial_proportions <- table(wf_dt2$iat_racial)/length(wf_dt2$iat_racial) # creating IAT racial table
(iatracial_percentages <- iatracial_proportions*100) # multiplying the table by 100 to get percentages
## 
##        21        22 
##  6.289308 23.899371

1. Defensiveness

hildebrand.rule(credibility, na.rm = TRUE)
##   Group     Ratio    Skew
## 1   xx  -0.115851 No Skew
hildebrand.rule(objective, na.rm = TRUE)
##   Group       Ratio    Skew
## 1   xx  0.008063028 No Skew
hildebrand.rule(valid, na.rm = TRUE)
##   Group      Ratio    Skew
## 1   xx  -0.1385317 No Skew
hildebrand.rule(useful, na.rm = TRUE)
##   Group      Ratio          Skew
## 1   xx  -0.2647419 Negative Skew
sd(credibility, na.rm = TRUE)
## [1] 1.708679
sd(objective, na.rm = TRUE)
## [1] 1.698944
sd(valid, na.rm = TRUE)
## [1] 1.705757
sd(useful, na.rm = TRUE)
## [1] 1.759273
hist(credibility)

hist(objective)

hist(valid)

hist(useful)

defensive <- select(wf_dt2, 85:88)
psych::alpha(defensive)
## 
## Reliability analysis   
## Call: psych::alpha(x = defensive)
## 
##   raw_alpha std.alpha G6(smc) average_r S/N    ase mean  sd median_r
##       0.92      0.92    0.91      0.74  11 0.0078  3.8 1.6     0.73
## 
##     95% confidence boundaries 
##          lower alpha upper
## Feldt      0.9  0.92  0.93
## Duhachek   0.9  0.92  0.93
## 
##  Reliability if an item is dropped:
##             raw_alpha std.alpha G6(smc) average_r  S/N alpha se  var.r med.r
## credibility      0.87      0.87    0.84      0.70  6.9   0.0126 0.0144  0.67
## objective        0.94      0.94    0.91      0.83 14.7   0.0063 0.0022  0.83
## valid            0.86      0.86    0.82      0.68  6.3   0.0135 0.0094  0.65
## useful           0.89      0.89    0.87      0.73  8.3   0.0109 0.0155  0.67
## 
##  Item statistics 
##               n raw.r std.r r.cor r.drop mean  sd
## credibility 284  0.93  0.93  0.91   0.86  3.8 1.7
## objective   283  0.81  0.82  0.70   0.68  4.0 1.7
## valid       283  0.94  0.94  0.94   0.90  3.8 1.7
## useful      283  0.90  0.90  0.85   0.81  3.5 1.8
## 
## Non missing response frequency for each item
##                1    2    3    4    5    6    7 miss
## credibility 0.15 0.11 0.10 0.27 0.23 0.08 0.06 0.11
## objective   0.13 0.06 0.12 0.30 0.17 0.14 0.07 0.11
## valid       0.15 0.10 0.17 0.23 0.19 0.11 0.05 0.11
## useful      0.18 0.15 0.15 0.21 0.19 0.07 0.06 0.11
wf_dt2$defensive <- rowMeans(wf_dt2[,85:88]) # creating defensiveness variable
myscale <- 1:7 #defining scale to reverse defensivness variable
wf_dt2 <- wf_dt2 %>%
  mutate(defensive_reverse = min(myscale) - defensive + max(myscale)) # reversing defensivness scale
wf_dt2$defensive_reverse_centered <- scale(wf_dt2$defensive_reverse, center = TRUE, scale = FALSE) #centering the variable
wf_dt2$defensive_reverse_Z <- scale(wf_dt2$defensive_reverse, center = TRUE, scale = TRUE) # z score defensiveness 
(def_r_mean =  mean(wf_dt2$defensive_reverse, na.rm = TRUE)) # mean defensivness
## [1] 4.241906
(def_r_sd =  sd(wf_dt2$defensive_reverse, na.rm = TRUE)) # sd defensiveness
## [1] 1.548895
hist(wf_dt2$defensive_reverse_Z) # histogram of defensive z scores

# Perform ANOVA for defensive_reverse by bias
summary((anova_defensive_reverse <- aov(defensive_reverse ~ as.factor(bias)*as.factor(brand_race), data = wf_dt2)))
##                                        Df Sum Sq Mean Sq F value      Pr(>F)
## as.factor(bias)                         1   56.5   56.49  25.670 0.000000746
## as.factor(brand_race)                   1    0.5    0.53   0.243       0.622
## as.factor(bias):as.factor(brand_race)   1    4.5    4.50   2.047       0.154
## Residuals                             274  603.0    2.20                    
##                                          
## as.factor(bias)                       ***
## as.factor(brand_race)                    
## as.factor(bias):as.factor(brand_race)    
## Residuals                                
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 40 observations deleted due to missingness
# Get summary of ANOVA
(tukey_posthoc <- TukeyHSD(anova_defensive_reverse))
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = defensive_reverse ~ as.factor(bias) * as.factor(brand_race), data = wf_dt2)
## 
## $`as.factor(bias)`
##          diff       lwr     upr     p adj
## 1-0 0.9044271 0.5530046 1.25585 0.0000007
## 
## $`as.factor(brand_race)`
##           diff        lwr       upr     p adj
## 1-0 0.08766191 -0.2626675 0.4379914 0.6226813
## 
## $`as.factor(bias):as.factor(brand_race)`
##               diff            lwr        upr     p adj
## 1:0-0:0  0.6523987 -0.00004964437  1.3048470 0.0500256
## 0:1-0:0 -0.1879277 -0.86613489865  0.4902796 0.8905901
## 1:1-0:0  0.9755824  0.31120396399  1.6399609 0.0010360
## 0:1-1:0 -0.8403263 -1.48166470937 -0.1989880 0.0044803
## 1:1-1:0  0.3231838 -0.30351282130  0.9498803 0.5427063
## 1:1-0:1  1.1635101  0.51003871844  1.8169815 0.0000378

2. Bias Awareness

hildebrand.rule(BA_1, na.rm = TRUE)
##   Group     Ratio    Skew
## 1   xx  0.1849701 No Skew
hildebrand.rule(BA_2, na.rm = TRUE)
##   Group     Ratio    Skew
## 1   xx  0.1529581 No Skew
hildebrand.rule(BA_3, na.rm = TRUE)
##   Group     Ratio    Skew
## 1   xx  -0.148806 No Skew
hildebrand.rule(BA_4, na.rm = TRUE)
##   Group        Ratio    Skew
## 1   xx  -0.006890947 No Skew
sd(BA_1, na.rm = TRUE)
## [1] 1.807888
sd(BA_2, na.rm = TRUE)
## [1] 1.786841
sd(BA_3, na.rm = TRUE)
## [1] 1.815091
sd(BA_4, na.rm = TRUE)
## [1] 1.866469
hist(BA_1)

hist(BA_2)

hist(BA_3)

hist(BA_4)

bias_aware <- select(wf_dt2, 14:17)
psych::alpha(bias_aware)
## 
## Reliability analysis   
## Call: psych::alpha(x = bias_aware)
## 
##   raw_alpha std.alpha G6(smc) average_r S/N   ase mean  sd median_r
##       0.82      0.82    0.79      0.53 4.5 0.017  3.6 1.5     0.54
## 
##     95% confidence boundaries 
##          lower alpha upper
## Feldt     0.78  0.82  0.85
## Duhachek  0.78  0.82  0.85
## 
##  Reliability if an item is dropped:
##      raw_alpha std.alpha G6(smc) average_r S/N alpha se  var.r med.r
## BA_1      0.81      0.81    0.75      0.59 4.3    0.019 0.0084  0.55
## BA_2      0.75      0.75    0.69      0.50 3.0    0.024 0.0123  0.55
## BA_3      0.71      0.71    0.63      0.45 2.5    0.028 0.0053  0.46
## BA_4      0.80      0.80    0.75      0.58 4.1    0.019 0.0141  0.58
## 
##  Item statistics 
##        n raw.r std.r r.cor r.drop mean  sd
## BA_1 302  0.75  0.75  0.61   0.55  3.3 1.8
## BA_2 302  0.83  0.83  0.76   0.68  3.3 1.8
## BA_3 302  0.88  0.88  0.85   0.76  3.7 1.8
## BA_4 302  0.77  0.76  0.63   0.57  4.0 1.9
## 
## Non missing response frequency for each item
##         1    2    3    4    5    6    7 miss
## BA_1 0.19 0.25 0.13 0.06 0.24 0.11 0.02 0.05
## BA_2 0.20 0.26 0.13 0.06 0.25 0.08 0.03 0.05
## BA_3 0.15 0.19 0.12 0.09 0.30 0.12 0.04 0.05
## BA_4 0.12 0.18 0.12 0.09 0.25 0.18 0.07 0.05
wf_dt2$bias_aware <- rowMeans(wf_dt2[,14:17])
wf_dt2$bias_aware_center <- scale(wf_dt2$bias_aware, center = TRUE, scale = FALSE) #centering the variable
wf_dt2$bias_aware_Z <- scale(wf_dt2$bias_aware, center = TRUE, scale = TRUE) #Z scores 
(BA_mean =  mean(wf_dt2$bias_aware, na.rm = TRUE)) # mean of bias awareness
## [1] 3.563742
(BA_sd =  sd(wf_dt2$bias_aware, na.rm = TRUE)) # sd of bias awareness
## [1] 1.463663
hist(wf_dt2$bias_aware_Z)

# Perform ANOVA for bias_aware by condition
summary((anova_bias_aware <- aov(bias_aware ~ as.factor(condition), data = wf_dt2)))
##                       Df Sum Sq Mean Sq F value Pr(>F)
## as.factor(condition)   3    5.1   1.714   0.798  0.496
## Residuals            298  639.7   2.147               
## 16 observations deleted due to missingness
# Run Tukey post hoc test
(tukey_posthoc <- TukeyHSD(anova_bias_aware))
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = bias_aware ~ as.factor(condition), data = wf_dt2)
## 
## $`as.factor(condition)`
##            diff        lwr       upr     p adj
## 1-0 -0.29681467 -0.9279424 0.3343131 0.6177784
## 2-0  0.01830357 -0.6012124 0.6378196 0.9998402
## 3-0 -0.18177656 -0.8049880 0.4414349 0.8750921
## 2-1  0.31511824 -0.2954025 0.9256390 0.5423101
## 3-1  0.11503812 -0.4992322 0.7293085 0.9626422
## 3-2 -0.20008013 -0.8024138 0.4022536 0.8263152

3. Emotions

hildebrand.rule(BMIS_lively_1, na.rm = TRUE)
##   Group    Ratio          Skew
## 1   xx  0.290114 Positive Skew
hildebrand.rule(BMIS_lively_2, na.rm = TRUE)
##   Group     Ratio          Skew
## 1   xx  0.3121008 Positive Skew
hildebrand.rule(BMIS_happy_1, na.rm = TRUE)
##   Group      Ratio          Skew
## 1   xx  -0.2571164 Negative Skew
hildebrand.rule(BMIS_happy_2, na.rm = TRUE)
##   Group      Ratio          Skew
## 1   xx  -0.4123413 Negative Skew
hildebrand.rule(BMIS_sad_1, na.rm = TRUE)
##   Group      Ratio    Skew
## 1   xx  -0.0779093 No Skew
hildebrand.rule(BMIS_sad_2, na.rm = TRUE)
##   Group       Ratio    Skew
## 1   xx  -0.01324874 No Skew
hildebrand.rule(BMIS_shame_1, na.rm = TRUE)
##   Group     Ratio          Skew
## 1   xx  0.7093866 Positive Skew
hildebrand.rule(BMIS_shame_2, na.rm = TRUE)
##   Group    Ratio          Skew
## 1   xx  0.803145 Positive Skew
hildebrand.rule(BMIS_guilt_1, na.rm = TRUE)
##   Group     Ratio          Skew
## 1   xx  0.7735714 Positive Skew
hildebrand.rule(BMIS_guilt_2, na.rm = TRUE)
##   Group     Ratio          Skew
## 1   xx  0.7938481 Positive Skew
hildebrand.rule(BMIS_tired_1, na.rm = TRUE)
##   Group     Ratio          Skew
## 1   xx  -0.299749 Negative Skew
hildebrand.rule(BMIS_tired_2, na.rm = TRUE)
##   Group      Ratio          Skew
## 1   xx  -0.3492858 Negative Skew
hildebrand.rule(BMIS_caring_1, na.rm = TRUE)
##   Group       Ratio    Skew
## 1   xx  -0.07747474 No Skew
hildebrand.rule(BMIS_caring_2, na.rm = TRUE)
##   Group      Ratio    Skew
## 1   xx  -0.1813282 No Skew
hildebrand.rule(BMIS_content_1, na.rm = TRUE)
##   Group      Ratio    Skew
## 1   xx  -0.1684255 No Skew
hildebrand.rule(BMIS_content_2, na.rm = TRUE)
##   Group      Ratio          Skew
## 1   xx  -0.2072466 Negative Skew
hildebrand.rule(BMIS_gloomy_1, na.rm = TRUE)
##   Group      Ratio    Skew
## 1   xx  -0.1031091 No Skew
hildebrand.rule(BMIS_gloomy_2, na.rm = TRUE)
##   Group       Ratio    Skew
## 1   xx  -0.09969695 No Skew
hildebrand.rule(BMIS_jittery_1, na.rm = TRUE)
##   Group     Ratio          Skew
## 1   xx  0.7373568 Positive Skew
hildebrand.rule(BMIS_jittery_2, na.rm = TRUE)
##   Group     Ratio          Skew
## 1   xx  0.7611705 Positive Skew
hildebrand.rule(BMIS_drowsy_1, na.rm = TRUE)
##   Group     Ratio    Skew
## 1   xx  0.1266293 No Skew
hildebrand.rule(BMIS_drowsy_2, na.rm = TRUE)
##   Group     Ratio    Skew
## 1   xx  0.1536428 No Skew
hildebrand.rule(BMIS_nervous_1, na.rm = TRUE)
##   Group      Ratio    Skew
## 1   xx  -0.1351261 No Skew
hildebrand.rule(BMIS_nervous_2, na.rm = TRUE)
##   Group      Ratio    Skew
## 1   xx  -0.1633531 No Skew
hildebrand.rule(BMIS_calm_1, na.rm = TRUE)
##   Group     Ratio    Skew
## 1   xx  0.1559281 No Skew
hildebrand.rule(BMIS_calm_2, na.rm = TRUE)
##   Group       Ratio    Skew
## 1   xx  -0.04192267 No Skew
hildebrand.rule(BMIS_jittery_1, na.rm = TRUE)
##   Group     Ratio          Skew
## 1   xx  0.7373568 Positive Skew
hildebrand.rule(BMIS_jittery_2, na.rm = TRUE)
##   Group     Ratio          Skew
## 1   xx  0.7611705 Positive Skew
hildebrand.rule(BMIS_loving_1, na.rm = TRUE)
##   Group      Ratio          Skew
## 1   xx  -0.2786378 Negative Skew
hildebrand.rule(BMIS_loving_2, na.rm = TRUE)
##   Group      Ratio          Skew
## 1   xx  -0.3080362 Negative Skew
hildebrand.rule(BMIS_fedup_1, na.rm = TRUE)
##   Group     Ratio          Skew
## 1   xx  0.8267106 Positive Skew
hildebrand.rule(BMIS_fedup_2, na.rm = TRUE)
##   Group     Ratio          Skew
## 1   xx  0.7907416 Positive Skew
sd(BMIS_lively_1, na.rm = TRUE)
## [1] 0.8924067
sd(BMIS_lively_2, na.rm = TRUE)
## [1] 0.8906292
sd(BMIS_happy_1, na.rm = TRUE)
## [1] 0.9004142
sd(BMIS_happy_2, na.rm = TRUE)
## [1] 0.9289656
sd(BMIS_sad_1, na.rm = TRUE)
## [1] 0.9905162
sd(BMIS_sad_2, na.rm = TRUE)
## [1] 1.023443
sd(BMIS_shame_1, na.rm = TRUE)
## [1] 0.8612123
sd(BMIS_shame_2, na.rm = TRUE)
## [1] 0.9655918
sd(BMIS_guilt_1, na.rm = TRUE)
## [1] 0.872888
sd(BMIS_guilt_2, na.rm = TRUE)
## [1] 0.9607782
sd(BMIS_tired_1, na.rm = TRUE)
## [1] 0.9685524
sd(BMIS_tired_2, na.rm = TRUE)
## [1] 1.025984
sd(BMIS_caring_1, na.rm = TRUE)
## [1] 0.913066
sd(BMIS_caring_2, na.rm = TRUE)
## [1] 0.9566599
sd(BMIS_content_1, na.rm = TRUE)
## [1] 0.9354656
sd(BMIS_content_2, na.rm = TRUE)
## [1] 0.9486772
sd(BMIS_gloomy_1, na.rm = TRUE)
## [1] 0.997913
sd(BMIS_gloomy_2, na.rm = TRUE)
## [1] 1.02351
sd(BMIS_jittery_1, na.rm = TRUE)
## [1] 0.8198224
sd(BMIS_jittery_2, na.rm = TRUE)
## [1] 0.8490325
sd(BMIS_drowsy_1, na.rm = TRUE)
## [1] 1.018976
sd(BMIS_drowsy_2, na.rm = TRUE)
## [1] 1.014901
sd(BMIS_nervous_1, na.rm = TRUE)
## [1] 0.9518324
sd(BMIS_nervous_2, na.rm = TRUE)
## [1] 0.9578181
sd(BMIS_calm_1, na.rm = TRUE)
## [1] 0.8454715
sd(BMIS_calm_2, na.rm = TRUE)
## [1] 0.8894503
sd(BMIS_jittery_1, na.rm = TRUE)
## [1] 0.8198224
sd(BMIS_jittery_2, na.rm = TRUE)
## [1] 0.8490325
sd(BMIS_loving_1, na.rm = TRUE)
## [1] 0.9756201
sd(BMIS_loving_2, na.rm = TRUE)
## [1] 0.9971792
sd(BMIS_fedup_1, na.rm = TRUE)
## [1] 0.9801367
sd(BMIS_fedup_2, na.rm = TRUE)
## [1] 0.8688993
hist(BMIS_lively_1, na.rm = TRUE)
## Warning in plot.window(xlim, ylim, "", ...): "na.rm" is not a graphical
## parameter
## Warning in title(main = main, sub = sub, xlab = xlab, ylab = ylab, ...):
## "na.rm" is not a graphical parameter
## Warning in axis(1, ...): "na.rm" is not a graphical parameter
## Warning in axis(2, at = yt, ...): "na.rm" is not a graphical parameter

hist(BMIS_lively_2, na.rm = TRUE)
## Warning in plot.window(xlim, ylim, "", ...): "na.rm" is not a graphical
## parameter
## Warning in title(main = main, sub = sub, xlab = xlab, ylab = ylab, ...):
## "na.rm" is not a graphical parameter
## Warning in axis(1, ...): "na.rm" is not a graphical parameter
## Warning in axis(2, at = yt, ...): "na.rm" is not a graphical parameter

hist(BMIS_happy_1, na.rm = TRUE)
## Warning in plot.window(xlim, ylim, "", ...): "na.rm" is not a graphical
## parameter
## Warning in title(main = main, sub = sub, xlab = xlab, ylab = ylab, ...):
## "na.rm" is not a graphical parameter
## Warning in axis(1, ...): "na.rm" is not a graphical parameter
## Warning in axis(2, at = yt, ...): "na.rm" is not a graphical parameter

hist(BMIS_happy_2, na.rm = TRUE)
## Warning in plot.window(xlim, ylim, "", ...): "na.rm" is not a graphical
## parameter
## Warning in title(main = main, sub = sub, xlab = xlab, ylab = ylab, ...):
## "na.rm" is not a graphical parameter
## Warning in axis(1, ...): "na.rm" is not a graphical parameter
## Warning in axis(2, at = yt, ...): "na.rm" is not a graphical parameter

hist(BMIS_sad_1, na.rm = TRUE)
## Warning in plot.window(xlim, ylim, "", ...): "na.rm" is not a graphical
## parameter
## Warning in title(main = main, sub = sub, xlab = xlab, ylab = ylab, ...):
## "na.rm" is not a graphical parameter
## Warning in axis(1, ...): "na.rm" is not a graphical parameter
## Warning in axis(2, at = yt, ...): "na.rm" is not a graphical parameter

hist(BMIS_sad_2, na.rm = TRUE)
## Warning in plot.window(xlim, ylim, "", ...): "na.rm" is not a graphical
## parameter
## Warning in title(main = main, sub = sub, xlab = xlab, ylab = ylab, ...):
## "na.rm" is not a graphical parameter
## Warning in axis(1, ...): "na.rm" is not a graphical parameter
## Warning in axis(2, at = yt, ...): "na.rm" is not a graphical parameter

hist(BMIS_shame_1, na.rm = TRUE)
## Warning in plot.window(xlim, ylim, "", ...): "na.rm" is not a graphical
## parameter
## Warning in title(main = main, sub = sub, xlab = xlab, ylab = ylab, ...):
## "na.rm" is not a graphical parameter
## Warning in axis(1, ...): "na.rm" is not a graphical parameter
## Warning in axis(2, at = yt, ...): "na.rm" is not a graphical parameter

hist(BMIS_shame_2, na.rm = TRUE)
## Warning in plot.window(xlim, ylim, "", ...): "na.rm" is not a graphical
## parameter
## Warning in title(main = main, sub = sub, xlab = xlab, ylab = ylab, ...):
## "na.rm" is not a graphical parameter
## Warning in axis(1, ...): "na.rm" is not a graphical parameter
## Warning in axis(2, at = yt, ...): "na.rm" is not a graphical parameter

hist(BMIS_guilt_1, na.rm = TRUE)
## Warning in plot.window(xlim, ylim, "", ...): "na.rm" is not a graphical
## parameter
## Warning in title(main = main, sub = sub, xlab = xlab, ylab = ylab, ...):
## "na.rm" is not a graphical parameter
## Warning in axis(1, ...): "na.rm" is not a graphical parameter
## Warning in axis(2, at = yt, ...): "na.rm" is not a graphical parameter

hist(BMIS_guilt_2, na.rm = TRUE)
## Warning in plot.window(xlim, ylim, "", ...): "na.rm" is not a graphical
## parameter
## Warning in title(main = main, sub = sub, xlab = xlab, ylab = ylab, ...):
## "na.rm" is not a graphical parameter
## Warning in axis(1, ...): "na.rm" is not a graphical parameter
## Warning in axis(2, at = yt, ...): "na.rm" is not a graphical parameter

hist(BMIS_tired_1, na.rm = TRUE)
## Warning in plot.window(xlim, ylim, "", ...): "na.rm" is not a graphical
## parameter
## Warning in title(main = main, sub = sub, xlab = xlab, ylab = ylab, ...):
## "na.rm" is not a graphical parameter
## Warning in axis(1, ...): "na.rm" is not a graphical parameter
## Warning in axis(2, at = yt, ...): "na.rm" is not a graphical parameter

hist(BMIS_tired_2, na.rm = TRUE)
## Warning in plot.window(xlim, ylim, "", ...): "na.rm" is not a graphical
## parameter
## Warning in title(main = main, sub = sub, xlab = xlab, ylab = ylab, ...):
## "na.rm" is not a graphical parameter
## Warning in axis(1, ...): "na.rm" is not a graphical parameter
## Warning in axis(2, at = yt, ...): "na.rm" is not a graphical parameter

hist(BMIS_caring_1, na.rm = TRUE)
## Warning in plot.window(xlim, ylim, "", ...): "na.rm" is not a graphical
## parameter
## Warning in title(main = main, sub = sub, xlab = xlab, ylab = ylab, ...):
## "na.rm" is not a graphical parameter
## Warning in axis(1, ...): "na.rm" is not a graphical parameter
## Warning in axis(2, at = yt, ...): "na.rm" is not a graphical parameter

hist(BMIS_caring_2, na.rm = TRUE)
## Warning in plot.window(xlim, ylim, "", ...): "na.rm" is not a graphical
## parameter
## Warning in title(main = main, sub = sub, xlab = xlab, ylab = ylab, ...):
## "na.rm" is not a graphical parameter
## Warning in axis(1, ...): "na.rm" is not a graphical parameter
## Warning in axis(2, at = yt, ...): "na.rm" is not a graphical parameter

hist(BMIS_content_1, na.rm = TRUE)
## Warning in plot.window(xlim, ylim, "", ...): "na.rm" is not a graphical
## parameter
## Warning in title(main = main, sub = sub, xlab = xlab, ylab = ylab, ...):
## "na.rm" is not a graphical parameter
## Warning in axis(1, ...): "na.rm" is not a graphical parameter
## Warning in axis(2, at = yt, ...): "na.rm" is not a graphical parameter

hist(BMIS_content_2, na.rm = TRUE)
## Warning in plot.window(xlim, ylim, "", ...): "na.rm" is not a graphical
## parameter
## Warning in title(main = main, sub = sub, xlab = xlab, ylab = ylab, ...):
## "na.rm" is not a graphical parameter
## Warning in axis(1, ...): "na.rm" is not a graphical parameter
## Warning in axis(2, at = yt, ...): "na.rm" is not a graphical parameter

hist(BMIS_gloomy_1, na.rm = TRUE)
## Warning in plot.window(xlim, ylim, "", ...): "na.rm" is not a graphical
## parameter
## Warning in title(main = main, sub = sub, xlab = xlab, ylab = ylab, ...):
## "na.rm" is not a graphical parameter
## Warning in axis(1, ...): "na.rm" is not a graphical parameter
## Warning in axis(2, at = yt, ...): "na.rm" is not a graphical parameter

hist(BMIS_gloomy_2, na.rm = TRUE)
## Warning in plot.window(xlim, ylim, "", ...): "na.rm" is not a graphical
## parameter
## Warning in title(main = main, sub = sub, xlab = xlab, ylab = ylab, ...):
## "na.rm" is not a graphical parameter
## Warning in axis(1, ...): "na.rm" is not a graphical parameter
## Warning in axis(2, at = yt, ...): "na.rm" is not a graphical parameter

hist(BMIS_jittery_1, na.rm = TRUE)
## Warning in plot.window(xlim, ylim, "", ...): "na.rm" is not a graphical
## parameter
## Warning in title(main = main, sub = sub, xlab = xlab, ylab = ylab, ...):
## "na.rm" is not a graphical parameter
## Warning in axis(1, ...): "na.rm" is not a graphical parameter
## Warning in axis(2, at = yt, ...): "na.rm" is not a graphical parameter

hist(BMIS_jittery_2, na.rm = TRUE)
## Warning in plot.window(xlim, ylim, "", ...): "na.rm" is not a graphical
## parameter
## Warning in title(main = main, sub = sub, xlab = xlab, ylab = ylab, ...):
## "na.rm" is not a graphical parameter
## Warning in axis(1, ...): "na.rm" is not a graphical parameter
## Warning in axis(2, at = yt, ...): "na.rm" is not a graphical parameter

hist(BMIS_drowsy_1, na.rm = TRUE)
## Warning in plot.window(xlim, ylim, "", ...): "na.rm" is not a graphical
## parameter
## Warning in title(main = main, sub = sub, xlab = xlab, ylab = ylab, ...):
## "na.rm" is not a graphical parameter
## Warning in axis(1, ...): "na.rm" is not a graphical parameter
## Warning in axis(2, at = yt, ...): "na.rm" is not a graphical parameter

hist(BMIS_drowsy_2, na.rm = TRUE)
## Warning in plot.window(xlim, ylim, "", ...): "na.rm" is not a graphical
## parameter
## Warning in title(main = main, sub = sub, xlab = xlab, ylab = ylab, ...):
## "na.rm" is not a graphical parameter
## Warning in axis(1, ...): "na.rm" is not a graphical parameter
## Warning in axis(2, at = yt, ...): "na.rm" is not a graphical parameter

hist(BMIS_nervous_1, na.rm = TRUE)
## Warning in plot.window(xlim, ylim, "", ...): "na.rm" is not a graphical
## parameter
## Warning in title(main = main, sub = sub, xlab = xlab, ylab = ylab, ...):
## "na.rm" is not a graphical parameter
## Warning in axis(1, ...): "na.rm" is not a graphical parameter
## Warning in axis(2, at = yt, ...): "na.rm" is not a graphical parameter

hist(BMIS_nervous_2, na.rm = TRUE)
## Warning in plot.window(xlim, ylim, "", ...): "na.rm" is not a graphical
## parameter
## Warning in title(main = main, sub = sub, xlab = xlab, ylab = ylab, ...):
## "na.rm" is not a graphical parameter
## Warning in axis(1, ...): "na.rm" is not a graphical parameter
## Warning in axis(2, at = yt, ...): "na.rm" is not a graphical parameter

hist(BMIS_calm_1, na.rm = TRUE)
## Warning in plot.window(xlim, ylim, "", ...): "na.rm" is not a graphical
## parameter
## Warning in title(main = main, sub = sub, xlab = xlab, ylab = ylab, ...):
## "na.rm" is not a graphical parameter
## Warning in axis(1, ...): "na.rm" is not a graphical parameter
## Warning in axis(2, at = yt, ...): "na.rm" is not a graphical parameter

hist(BMIS_calm_2, na.rm = TRUE)
## Warning in plot.window(xlim, ylim, "", ...): "na.rm" is not a graphical
## parameter
## Warning in title(main = main, sub = sub, xlab = xlab, ylab = ylab, ...):
## "na.rm" is not a graphical parameter
## Warning in axis(1, ...): "na.rm" is not a graphical parameter
## Warning in axis(2, at = yt, ...): "na.rm" is not a graphical parameter

hist(BMIS_jittery_1, na.rm = TRUE)
## Warning in plot.window(xlim, ylim, "", ...): "na.rm" is not a graphical
## parameter
## Warning in title(main = main, sub = sub, xlab = xlab, ylab = ylab, ...):
## "na.rm" is not a graphical parameter
## Warning in axis(1, ...): "na.rm" is not a graphical parameter
## Warning in axis(2, at = yt, ...): "na.rm" is not a graphical parameter

hist(BMIS_jittery_2, na.rm = TRUE)
## Warning in plot.window(xlim, ylim, "", ...): "na.rm" is not a graphical
## parameter
## Warning in title(main = main, sub = sub, xlab = xlab, ylab = ylab, ...):
## "na.rm" is not a graphical parameter
## Warning in axis(1, ...): "na.rm" is not a graphical parameter
## Warning in axis(2, at = yt, ...): "na.rm" is not a graphical parameter

hist(BMIS_loving_1, na.rm = TRUE)
## Warning in plot.window(xlim, ylim, "", ...): "na.rm" is not a graphical
## parameter
## Warning in title(main = main, sub = sub, xlab = xlab, ylab = ylab, ...):
## "na.rm" is not a graphical parameter
## Warning in axis(1, ...): "na.rm" is not a graphical parameter
## Warning in axis(2, at = yt, ...): "na.rm" is not a graphical parameter

hist(BMIS_loving_2, na.rm = TRUE)
## Warning in plot.window(xlim, ylim, "", ...): "na.rm" is not a graphical
## parameter
## Warning in title(main = main, sub = sub, xlab = xlab, ylab = ylab, ...):
## "na.rm" is not a graphical parameter
## Warning in axis(1, ...): "na.rm" is not a graphical parameter
## Warning in axis(2, at = yt, ...): "na.rm" is not a graphical parameter

hist(BMIS_fedup_1, na.rm = TRUE)
## Warning in plot.window(xlim, ylim, "", ...): "na.rm" is not a graphical
## parameter
## Warning in title(main = main, sub = sub, xlab = xlab, ylab = ylab, ...):
## "na.rm" is not a graphical parameter
## Warning in axis(1, ...): "na.rm" is not a graphical parameter
## Warning in axis(2, at = yt, ...): "na.rm" is not a graphical parameter

hist(BMIS_fedup_2, na.rm = TRUE)
## Warning in plot.window(xlim, ylim, "", ...): "na.rm" is not a graphical
## parameter
## Warning in title(main = main, sub = sub, xlab = xlab, ylab = ylab, ...):
## "na.rm" is not a graphical parameter
## Warning in axis(1, ...): "na.rm" is not a graphical parameter
## Warning in axis(2, at = yt, ...): "na.rm" is not a graphical parameter

wf_dt2$BMIS_guilt_1_center <- scale(wf_dt2$BMIS_guilt_1, center = TRUE, scale = FALSE) #centering the variable
wf_dt2$BMIS_guilt_1_Z <- scale(wf_dt2$BMIS_guilt_1, center = TRUE, scale = TRUE) #Z score
wf_dt2$BMIS_shame_1_center <- scale(wf_dt2$BMIS_shame_1, center = TRUE, scale = FALSE) #centering the variable
wf_dt2$BMIS_shame_1_Z <- scale(wf_dt2$BMIS_shame_1, center = TRUE, scale = TRUE) #Z score
wf_dt2$BMIS_sad_1_center <- scale(wf_dt2$BMIS_sad_1, center = TRUE, scale = FALSE) #centering the variable
wf_dt2$BMIS_sad_1_Z <- scale(wf_dt2$BMIS_sad_1, center = TRUE, scale = TRUE) #Z score
wf_dt2$BMIS_guilt_2_center <- scale(wf_dt2$BMIS_guilt_2, center = TRUE, scale = FALSE) #centering the variable
wf_dt2$BMIS_guilt_2_Z <- scale(wf_dt2$BMIS_guilt_2, center = TRUE, scale = TRUE) #Z score
wf_dt2$BMIS_shame_2_center <- scale(wf_dt2$BMIS_shame_2, center = TRUE, scale = FALSE) #centering the variable
wf_dt2$BMIS_shame_2_Z <- scale(wf_dt2$BMIS_shame_2, center = TRUE, scale = TRUE) #Z score
wf_dt2$BMIS_sad_2_center <- scale(wf_dt2$BMIS_sad_2, center = TRUE, scale = FALSE) #centering the variable
wf_dt2$BMIS_sad_2_Z <- scale(wf_dt2$BMIS_sad_2, center = TRUE, scale = TRUE) #Z score
(guilty_mean = mean(wf_dt2$BMIS_guilt, na.rm = TRUE))
## Warning: Unknown or uninitialised column: `BMIS_guilt`.
## Warning in mean.default(wf_dt2$BMIS_guilt, na.rm = TRUE): argument is not
## numeric or logical: returning NA
## [1] NA
(guilty_sd = sd(wf_dt2$BMIS_guilt, na.rm = TRUE))
## Warning: Unknown or uninitialised column: `BMIS_guilt`.
## [1] NA
(shame_mean = mean(wf_dt2$BMIS_shame, na.rm = TRUE))
## Warning: Unknown or uninitialised column: `BMIS_shame`.
## Warning in mean.default(wf_dt2$BMIS_shame, na.rm = TRUE): argument is not
## numeric or logical: returning NA
## [1] NA
(shame_sd = sd(wf_dt2$BMIS_shame, na.rm = TRUE))
## Warning: Unknown or uninitialised column: `BMIS_shame`.
## [1] NA
guilt_shame_sad <- select(wf_dt2, BMIS_sad_2, BMIS_shame_2, BMIS_guilt_2) # dropping certain variables for the alpha
psych::alpha(guilt_shame_sad)
## 
## Reliability analysis   
## Call: psych::alpha(x = guilt_shame_sad)
## 
##   raw_alpha std.alpha G6(smc) average_r S/N  ase mean  sd median_r
##        0.9       0.9     0.9      0.76 9.5 0.01  1.8 0.9     0.68
## 
##     95% confidence boundaries 
##          lower alpha upper
## Feldt     0.88   0.9  0.92
## Duhachek  0.88   0.9  0.92
## 
##  Reliability if an item is dropped:
##              raw_alpha std.alpha G6(smc) average_r  S/N alpha se var.r med.r
## BMIS_sad_2        0.97      0.97    0.94      0.94 29.3   0.0037    NA  0.94
## BMIS_shame_2      0.80      0.80    0.66      0.66  4.0   0.0226    NA  0.66
## BMIS_guilt_2      0.81      0.81    0.68      0.68  4.2   0.0216    NA  0.68
## 
##  Item statistics 
##                n raw.r std.r r.cor r.drop mean   sd
## BMIS_sad_2   286  0.86  0.85  0.69   0.68  2.0 1.02
## BMIS_shame_2 285  0.95  0.95  0.96   0.88  1.8 0.96
## BMIS_guilt_2 286  0.94  0.95  0.95   0.87  1.8 0.96
## 
## Non missing response frequency for each item
##                 1    2    3    4 miss
## BMIS_sad_2   0.44 0.22 0.25 0.09  0.1
## BMIS_shame_2 0.54 0.20 0.20 0.06  0.1
## BMIS_guilt_2 0.55 0.20 0.20 0.06  0.1
wf_dt2$guilt_shame_sad <- rowMeans(guilt_shame_sad)
wf_dt2$guilt_shame_sad_center <- scale(wf_dt2$guilt_shame_sad, center = TRUE, scale = FALSE) #centering the variable
wf_dt2$guilt_shame_sad_Z <- scale(wf_dt2$guilt_shame_sad, center = TRUE, scale = TRUE) #Z scores 
wf_dt2 <- wf_dt2 %>% mutate(shame_discrepancy = BMIS_shame_2_Z - BMIS_shame_1_Z) # higher scores indicate an increase in shame, lower scores indicate decrease in shame
# Independent samples t-test for BMIS_shame by condition
(t_test_BMIS_shame_2 <- t.test(BMIS_shame_2 ~ bias, var.equal = TRUE, data = wf_dt2))
## 
##  Two Sample t-test
## 
## data:  BMIS_shame_2 by bias
## t = -4.6422, df = 283, p-value = 0.000005282
## alternative hypothesis: true difference in means between group 0 and group 1 is not equal to 0
## 95 percent confidence interval:
##  -0.7306252 -0.2955185
## sample estimates:
## mean in group 0 mean in group 1 
##        1.500000        2.013072
# Calculating Cohen's d for BMIS_shame by condition
(cohen_d_BMIS_shame_2 <- cohensD(BMIS_shame_2 ~ bias, data = wf_dt2))
## [1] 0.5514569
# Calculating standard deviations of BMIS_shame by condition
(sd_by_condition <- aggregate(BMIS_shame_2 ~ bias, data = wf_dt2, FUN = sd))
##   bias BMIS_shame_2
## 1    0    0.7666722
## 2    1    1.0512332
# Independent samples t-test for BMIS_shame by condition
(t_test_BMIS_shame_2 <- t.test(BMIS_shame_1 ~ bias, var.equal = TRUE, data = wf_dt2))
## 
##  Two Sample t-test
## 
## data:  BMIS_shame_1 by bias
## t = 0.23429, df = 300, p-value = 0.8149
## alternative hypothesis: true difference in means between group 0 and group 1 is not equal to 0
## 95 percent confidence interval:
##  -0.1710664  0.2173041
## sample estimates:
## mean in group 0 mean in group 1 
##        1.618056        1.594937
# Calculating Cohen's d for BMIS_shame by condition
(cohen_d_BMIS_shame_2 <- cohensD(BMIS_shame_1 ~ bias, data = wf_dt2))
## [1] 0.02699276
# Calculating standard deviations of BMIS_shame by condition
(sd_by_condition <- aggregate(BMIS_shame_1 ~ bias, data = wf_dt2, FUN = sd))
##   bias BMIS_shame_1
## 1    0    0.8609356
## 2    1    0.8524074
# One-way ANOVA for SE_2 by bias
summary((anova_SE2 <- aov(SE_2 ~ as.factor(bias)*as.factor(brand_race), data = wf_dt2)))
##                                        Df Sum Sq Mean Sq F value Pr(>F)
## as.factor(bias)                         1    1.5  1.4913   0.850  0.357
## as.factor(brand_race)                   1    1.9  1.9410   1.107  0.294
## as.factor(bias):as.factor(brand_race)   1    0.5  0.5037   0.287  0.592
## Residuals                             282  494.7  1.7541               
## 32 observations deleted due to missingness
# Tukey post hoc test
(tukey_SE2 <- TukeyHSD(anova_SE2))
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = SE_2 ~ as.factor(bias) * as.factor(brand_race), data = wf_dt2)
## 
## $`as.factor(bias)`
##           diff       lwr       upr    p adj
## 1-0 -0.1447737 -0.453845 0.1642976 0.357299
## 
## $`as.factor(brand_race)`
##          diff        lwr       upr     p adj
## 1-0 0.1646979 -0.1436244 0.4730201 0.2939405
## 
## $`as.factor(bias):as.factor(brand_race)`
##                diff        lwr       upr     p adj
## 1:0-0:0 -0.22516026 -0.8024587 0.3521382 0.7448843
## 0:1-0:0  0.07472826 -0.5192969 0.6687534 0.9881077
## 1:1-0:0  0.01791667 -0.5645624 0.6003957 0.9998195
## 0:1-1:0  0.29988852 -0.2658039 0.8655809 0.5192046
## 1:1-1:0  0.24307692 -0.3104787 0.7966326 0.6682772
## 1:1-0:1 -0.05681159 -0.6277899 0.5141667 0.9940277
# One-way ANOVA for SE_2 by bias
summary((anova_SE2 <- aov(SE_2 ~ as.factor(condition), data = wf_dt2)))
##                       Df Sum Sq Mean Sq F value Pr(>F)
## as.factor(condition)   3    3.9   1.312   0.748  0.524
## Residuals            282  494.7   1.754               
## 32 observations deleted due to missingness
# Tukey post hoc test
(tukey_SE2 <- TukeyHSD(anova_SE2))
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = SE_2 ~ as.factor(condition), data = wf_dt2)
## 
## $`as.factor(condition)`
##            diff        lwr       upr     p adj
## 1-0  0.07472826 -0.5192969 0.6687534 0.9881077
## 2-0 -0.22516026 -0.8024587 0.3521382 0.7448843
## 3-0  0.01791667 -0.5645624 0.6003957 0.9998195
## 2-1 -0.29988852 -0.8655809 0.2658039 0.5192046
## 3-1 -0.05681159 -0.6277899 0.5141667 0.9940277
## 3-2  0.24307692 -0.3104787 0.7966326 0.6682772
# Independent samples t-test for BMIS_shame by condition
(t_test_BMIS_shame_2 <- t.test(SE_2 ~ bias, var.equal = TRUE, data = wf_dt2))
## 
##  Two Sample t-test
## 
## data:  SE_2 by bias
## t = 0.92302, df = 284, p-value = 0.3568
## alternative hypothesis: true difference in means between group 0 and group 1 is not equal to 0
## 95 percent confidence interval:
##  -0.1639581  0.4535055
## sample estimates:
## mean in group 0 mean in group 1 
##        3.007519        2.862745
# Calculating Cohen's d for BMIS_shame by condition
(cohen_d_BMIS_shame_2 <- cohensD(SE_2 ~ bias, data = wf_dt2))
## [1] 0.1094265
# Calculating standard deviations of BMIS_shame by condition
(sd_by_condition <- aggregate(SE_2 ~ bias, data = wf_dt2, FUN = sd))
##   bias     SE_2
## 1    0 1.264289
## 2    1 1.371989
wf_dt2$SE_2_Z <- scale(wf_dt2$SE_2, center = TRUE, scale = TRUE) #Z scores
wf_dt2$SE_1_Z <- scale(wf_dt2$SE1, center = TRUE, scale = TRUE) #Z scores
wf_dt2 <- wf_dt2 %>% mutate(SE_change = SE_2_Z - SE_1_Z) # higher scores indicate increases while negative score indicate decreases

4. Model Regressions

# One-way ANOVA for purchase by interaction
summary((anova_purchase <- aov(purchase ~ as.factor(bias)*as.factor(brand_race), data = wf_dt2)))
##                                        Df Sum Sq Mean Sq F value    Pr(>F)    
## as.factor(bias)                         1   32.9   32.91   8.053   0.00484 ** 
## as.factor(brand_race)                   1   81.7   81.67  19.985 0.0000109 ***
## as.factor(bias):as.factor(brand_race)   1   18.4   18.44   4.511   0.03446 *  
## Residuals                             314 1283.2    4.09                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# Tukey post hoc test
(tukey_purchase <- TukeyHSD(anova_purchase))
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = purchase ~ as.factor(bias) * as.factor(brand_race), data = wf_dt2)
## 
## $`as.factor(bias)`
##          diff       lwr     upr     p adj
## 1-0 0.6434335 0.1973273 1.08954 0.0048373
## 
## $`as.factor(brand_race)`
##         diff      lwr      upr    p adj
## 1-0 1.014068 0.567538 1.460599 0.000011
## 
## $`as.factor(bias):as.factor(brand_race)`
##              diff        lwr       upr     p adj
## 1:0-0:0 1.0729651  0.2619190 1.8840112 0.0039778
## 0:1-0:0 1.4956003  0.6676950 2.3235055 0.0000270
## 1:1-0:0 1.6040549  0.7876396 2.4204701 0.0000040
## 0:1-1:0 0.4226351 -0.4195067 1.2647769 0.5660145
## 1:1-1:0 0.5310897 -0.2997590 1.3619385 0.3516638
## 1:1-0:1 0.1084546 -0.7388593 0.9557685 0.9875159

5. Shame Regressions

summary(lm(BMIS_shame_2_Z ~ bias_aware_Z*as.factor(bias), wf_dt2)) #bias aware sig on its own and interaction
## 
## Call:
## lm(formula = BMIS_shame_2_Z ~ bias_aware_Z * as.factor(bias), 
##     data = wf_dt2)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.8994 -0.5751 -0.4242  0.5885  2.5843 
## 
## Coefficients:
##                               Estimate Std. Error t value   Pr(>|t|)    
## (Intercept)                   -0.28392    0.08072  -3.518   0.000508 ***
## bias_aware_Z                   0.06689    0.08350   0.801   0.423730    
## as.factor(bias)1               0.51996    0.11016   4.720 0.00000372 ***
## bias_aware_Z:as.factor(bias)1  0.29877    0.11051   2.704   0.007279 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9269 on 281 degrees of freedom
##   (33 observations deleted due to missingness)
## Multiple R-squared:  0.1499, Adjusted R-squared:  0.1408 
## F-statistic: 16.51 on 3 and 281 DF,  p-value: 0.0000000006535

BA interacts with bias condition to predit increased shame.

summary(lm(BMIS_shame_2_Z ~ defensive_reverse_Z*as.factor(bias), wf_dt2)) #defensiveness on its own not sig
## 
## Call:
## lm(formula = BMIS_shame_2_Z ~ defensive_reverse_Z * as.factor(bias), 
##     data = wf_dt2)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.4913 -0.7599 -0.3942  0.7591  2.5916 
## 
## Coefficients:
##                                      Estimate Std. Error t value   Pr(>|t|)    
## (Intercept)                          -0.24669    0.08990  -2.744    0.00647 ** 
## defensive_reverse_Z                   0.11327    0.09187   1.233    0.21865    
## as.factor(bias)1                      0.55590    0.12125   4.585 0.00000693 ***
## defensive_reverse_Z:as.factor(bias)1 -0.29353    0.12194  -2.407    0.01674 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9609 on 273 degrees of freedom
##   (41 observations deleted due to missingness)
## Multiple R-squared:  0.09452,    Adjusted R-squared:  0.08457 
## F-statistic: 9.499 on 3 and 273 DF,  p-value: 0.000005463
summary(lm(BMIS_shame_2_Z ~ BMIS_shame_1_Z, wf_dt2)) # t1 and t2 are somewhat related
## 
## Call:
## lm(formula = BMIS_shame_2_Z ~ BMIS_shame_1_Z, data = wf_dt2)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.9437 -0.3044 -0.3044  0.1321  2.8093 
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)    -0.003743   0.042316  -0.088                0.93    
## BMIS_shame_1_Z  0.700904   0.042380  16.538 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.7144 on 283 degrees of freedom
##   (33 observations deleted due to missingness)
## Multiple R-squared:  0.4915, Adjusted R-squared:  0.4897 
## F-statistic: 273.5 on 1 and 283 DF,  p-value: < 0.00000000000000022
summary(lm(BMIS_shame_2_Z ~ bias_discrepancy_Z*bias_aware_Z, wf_dt2)) # bias discrepancy is what predicts shame, it is sig when bias condition is in the model, but they do not interact (close at .24 with a postive ineraction). Does interact with bias aware such that those who were more bias aware reported a larger discrepancy and ultimatley reported more shame
## 
## Call:
## lm(formula = BMIS_shame_2_Z ~ bias_discrepancy_Z * bias_aware_Z, 
##     data = wf_dt2)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.6095 -0.6912 -0.3006  0.6511  2.6177 
## 
## Coefficients:
##                                  Estimate Std. Error t value   Pr(>|t|)    
## (Intercept)                      0.004705   0.055763   0.084      0.933    
## bias_discrepancy_Z              -0.257326   0.056514  -4.553 0.00000788 ***
## bias_aware_Z                     0.267658   0.055618   4.812 0.00000244 ***
## bias_discrepancy_Z:bias_aware_Z -0.054683   0.060351  -0.906      0.366    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9358 on 281 degrees of freedom
##   (33 observations deleted due to missingness)
## Multiple R-squared:  0.1335, Adjusted R-squared:  0.1242 
## F-statistic: 14.43 on 3 and 281 DF,  p-value: 0.000000009085
summary(lm(shame_discrepancy ~ bias_discrepancy_Z, wf_dt2)) # larger bias discrepancy was associated with a greater change in shame, does not interact with as.factor bias
## 
## Call:
## lm(formula = shame_discrepancy ~ bias_discrepancy_Z, data = wf_dt2)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2.37177 -0.29593 -0.03296  0.21531  2.83252 
## 
## Coefficients:
##                     Estimate Std. Error t value    Pr(>|t|)    
## (Intercept)        -0.005542   0.043755  -0.127       0.899    
## bias_discrepancy_Z -0.232661   0.043760  -5.317 0.000000215 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.7387 on 283 degrees of freedom
##   (33 observations deleted due to missingness)
## Multiple R-squared:  0.09082,    Adjusted R-squared:  0.0876 
## F-statistic: 28.27 on 1 and 283 DF,  p-value: 0.0000002146
summary(lm(shame_discrepancy ~ bias_aware_Z*as.factor(bias), wf_dt2)) # bias awareness by itself non sig, but interacts with bias condition to predict greater increases in shame 
## 
## Call:
## lm(formula = shame_discrepancy ~ bias_aware_Z * as.factor(bias), 
##     data = wf_dt2)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2.33552 -0.38817  0.00329  0.25513  2.55957 
## 
## Coefficients:
##                               Estimate Std. Error t value      Pr(>|t|)    
## (Intercept)                   -0.29818    0.06271  -4.755 0.00000317436 ***
## bias_aware_Z                  -0.11342    0.06487  -1.748        0.0815 .  
## as.factor(bias)1               0.53949    0.08558   6.304 0.00000000112 ***
## bias_aware_Z:as.factor(bias)1  0.22142    0.08586   2.579        0.0104 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.7201 on 281 degrees of freedom
##   (33 observations deleted due to missingness)
## Multiple R-squared:  0.1419, Adjusted R-squared:  0.1328 
## F-statistic:  15.5 on 3 and 281 DF,  p-value: 0.000000002351
summary(lm(shame_discrepancy ~ defensive_reverse_Z, wf_dt2)) # defensiveness does not predict changes in shame 
## 
## Call:
## lm(formula = shame_discrepancy ~ defensive_reverse_Z, data = wf_dt2)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2.43388 -0.23859 -0.10711  0.00122  3.10294 
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)
## (Intercept)         -0.00155    0.04668  -0.033    0.974
## defensive_reverse_Z  0.07457    0.04675   1.595    0.112
## 
## Residual standard error: 0.7768 on 275 degrees of freedom
##   (41 observations deleted due to missingness)
## Multiple R-squared:  0.009166,   Adjusted R-squared:  0.005563 
## F-statistic: 2.544 on 1 and 275 DF,  p-value: 0.1119
summary(lm(shame_discrepancy ~ SE_change, wf_dt2)) # defensiveness does not predict changes in shame 
## 
## Call:
## lm(formula = shame_discrepancy ~ SE_change, data = wf_dt2)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.4376 -0.2412 -0.1151  0.1553  3.0150 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)  
## (Intercept) -0.003262   0.045435  -0.072   0.9428  
## SE_change   -0.357664   0.148738  -2.405   0.0168 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.7669 on 283 degrees of freedom
##   (33 observations deleted due to missingness)
## Multiple R-squared:  0.02002,    Adjusted R-squared:  0.01656 
## F-statistic: 5.782 on 1 and 283 DF,  p-value: 0.01683

6. Defensiveness Regressions

summary(lm(defensive_reverse_Z ~ bias_aware_Z, wf_dt2)) 
## 
## Call:
## lm(formula = defensive_reverse_Z ~ bias_aware_Z, data = wf_dt2)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2.34709 -0.70563 -0.05711  0.74082  1.97285 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)  
## (Intercept)  -0.0001677  0.0594346  -0.003   0.9978  
## bias_aware_Z -0.1451347  0.0589087  -2.464   0.0144 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.991 on 276 degrees of freedom
##   (40 observations deleted due to missingness)
## Multiple R-squared:  0.02152,    Adjusted R-squared:  0.01797 
## F-statistic:  6.07 on 1 and 276 DF,  p-value: 0.01436

BA interacts with bias condition to predict decreased defensiveness.

6. Purchase Intentions

summary(lm(purchase_Z ~ as.factor(bias)*as.factor(brand_race) + defensive_reverse_Z, wf_dt2))
## 
## Call:
## lm(formula = purchase_Z ~ as.factor(bias) * as.factor(brand_race) + 
##     defensive_reverse_Z, data = wf_dt2)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.2422 -0.5772  0.1151  0.6103  1.6169 
## 
## Coefficients:
##                                         Estimate Std. Error t value Pr(>|t|)   
## (Intercept)                             -0.08270    0.10695  -0.773  0.44002   
## as.factor(bias)1                         0.18479    0.14389   1.284  0.20013   
## as.factor(brand_race)1                   0.35798    0.14791   2.420  0.01617 * 
## defensive_reverse_Z                     -0.16558    0.05270  -3.142  0.00186 **
## as.factor(bias)1:as.factor(brand_race)1 -0.02875    0.20196  -0.142  0.88691   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8355 on 273 degrees of freedom
##   (40 observations deleted due to missingness)
## Multiple R-squared:  0.07284,    Adjusted R-squared:  0.05926 
## F-statistic: 5.362 on 4 and 273 DF,  p-value: 0.0003594
summary(lm(purchase_Z ~ as.factor(bias)*defensive_reverse_Z, wf_dt2))
## 
## Call:
## lm(formula = purchase_Z ~ as.factor(bias) * defensive_reverse_Z, 
##     data = wf_dt2)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2.26392 -0.53755 -0.01177  0.64252  1.48409 
## 
## Coefficients:
##                                      Estimate Std. Error t value  Pr(>|t|)    
## (Intercept)                           0.04760    0.07829   0.608   0.54364    
## as.factor(bias)1                      0.17488    0.10569   1.655   0.09914 .  
## defensive_reverse_Z                  -0.33786    0.08001  -4.223 0.0000329 ***
## as.factor(bias)1:defensive_reverse_Z  0.31227    0.10629   2.938   0.00359 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8386 on 274 degrees of freedom
##   (40 observations deleted due to missingness)
## Multiple R-squared:  0.06272,    Adjusted R-squared:  0.05246 
## F-statistic: 6.112 on 3 and 274 DF,  p-value: 0.0004882
summary(lm(purchase_Z ~ defensive_reverse_Z*as.factor(bias)*as.factor(brand_race), wf_dt2))
## 
## Call:
## lm(formula = purchase_Z ~ defensive_reverse_Z * as.factor(bias) * 
##     as.factor(brand_race), data = wf_dt2)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2.37122 -0.45834  0.06874  0.59389  1.62104 
## 
## Coefficients:
##                                                             Estimate Std. Error
## (Intercept)                                                 -0.13578    0.10843
## defensive_reverse_Z                                         -0.37578    0.11756
## as.factor(bias)1                                             0.23896    0.14365
## as.factor(brand_race)1                                       0.36614    0.15340
## defensive_reverse_Z:as.factor(bias)1                         0.20370    0.14832
## defensive_reverse_Z:as.factor(brand_race)1                   0.09006    0.15797
## as.factor(bias)1:as.factor(brand_race)1                     -0.15253    0.20830
## defensive_reverse_Z:as.factor(bias)1:as.factor(brand_race)1  0.21994    0.21085
##                                                             t value Pr(>|t|)   
## (Intercept)                                                  -1.252  0.21159   
## defensive_reverse_Z                                          -3.197  0.00156 **
## as.factor(bias)1                                              1.664  0.09737 . 
## as.factor(brand_race)1                                        2.387  0.01768 * 
## defensive_reverse_Z:as.factor(bias)1                          1.373  0.17077   
## defensive_reverse_Z:as.factor(brand_race)1                    0.570  0.56909   
## as.factor(bias)1:as.factor(brand_race)1                      -0.732  0.46462   
## defensive_reverse_Z:as.factor(bias)1:as.factor(brand_race)1   1.043  0.29783   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8212 on 270 degrees of freedom
##   (40 observations deleted due to missingness)
## Multiple R-squared:  0.1143, Adjusted R-squared:  0.09132 
## F-statistic: 4.977 on 7 and 270 DF,  p-value: 0.00002575
summary(lm(purchase_Z ~ defensive_reverse_Z + as.factor(brand_race), wf_dt2))
## 
## Call:
## lm(formula = purchase_Z ~ defensive_reverse_Z + as.factor(brand_race), 
##     data = wf_dt2)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.2881 -0.6357  0.1003  0.6190  1.6785 
## 
## Coefficients:
##                        Estimate Std. Error t value Pr(>|t|)    
## (Intercept)             0.02069    0.07071   0.293 0.770041    
## defensive_reverse_Z    -0.14130    0.05027  -2.811 0.005298 ** 
## as.factor(brand_race)1  0.33556    0.10036   3.343 0.000942 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8365 on 275 degrees of freedom
##   (40 observations deleted due to missingness)
## Multiple R-squared:  0.06382,    Adjusted R-squared:  0.05701 
## F-statistic: 9.373 on 2 and 275 DF,  p-value: 0.0001153
summary(lm(purchase_Z ~ defensive_reverse_Z*as.factor(brand_race)*as.factor(bias), wf_dt2))
## 
## Call:
## lm(formula = purchase_Z ~ defensive_reverse_Z * as.factor(brand_race) * 
##     as.factor(bias), data = wf_dt2)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2.37122 -0.45834  0.06874  0.59389  1.62104 
## 
## Coefficients:
##                                                             Estimate Std. Error
## (Intercept)                                                 -0.13578    0.10843
## defensive_reverse_Z                                         -0.37578    0.11756
## as.factor(brand_race)1                                       0.36614    0.15340
## as.factor(bias)1                                             0.23896    0.14365
## defensive_reverse_Z:as.factor(brand_race)1                   0.09006    0.15797
## defensive_reverse_Z:as.factor(bias)1                         0.20370    0.14832
## as.factor(brand_race)1:as.factor(bias)1                     -0.15253    0.20830
## defensive_reverse_Z:as.factor(brand_race)1:as.factor(bias)1  0.21994    0.21085
##                                                             t value Pr(>|t|)   
## (Intercept)                                                  -1.252  0.21159   
## defensive_reverse_Z                                          -3.197  0.00156 **
## as.factor(brand_race)1                                        2.387  0.01768 * 
## as.factor(bias)1                                              1.664  0.09737 . 
## defensive_reverse_Z:as.factor(brand_race)1                    0.570  0.56909   
## defensive_reverse_Z:as.factor(bias)1                          1.373  0.17077   
## as.factor(brand_race)1:as.factor(bias)1                      -0.732  0.46462   
## defensive_reverse_Z:as.factor(brand_race)1:as.factor(bias)1   1.043  0.29783   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8212 on 270 degrees of freedom
##   (40 observations deleted due to missingness)
## Multiple R-squared:  0.1143, Adjusted R-squared:  0.09132 
## F-statistic: 4.977 on 7 and 270 DF,  p-value: 0.00002575
summary(lm(rr_purchase ~ defensive_reverse_Z + as.factor(bias), wf_dt2))
## 
## Call:
## lm(formula = rr_purchase ~ defensive_reverse_Z + as.factor(bias), 
##     data = wf_dt2)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.9287 -0.5855  0.0003  1.4145  2.8524 
## 
## Coefficients:
##                     Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)           4.3587     0.2191  19.896 <0.0000000000000002 ***
## defensive_reverse_Z  -0.2016     0.1566  -1.288               0.200    
## as.factor(bias)1      0.1479     0.3140   0.471               0.638    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.708 on 134 degrees of freedom
##   (181 observations deleted due to missingness)
## Multiple R-squared:  0.01222,    Adjusted R-squared:  -0.002519 
## F-statistic: 0.8291 on 2 and 134 DF,  p-value: 0.4387
summary(lm(rr_purchase ~ defensive_reverse_Z*as.factor(bias), wf_dt2))
## 
## Call:
## lm(formula = rr_purchase ~ defensive_reverse_Z * as.factor(bias), 
##     data = wf_dt2)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.3017 -0.7455  0.0462  1.3015  2.9132 
## 
## Coefficients:
##                                      Estimate Std. Error t value
## (Intercept)                            4.1821     0.2184  19.150
## defensive_reverse_Z                   -0.6959     0.2140  -3.252
## as.factor(bias)1                       0.1384     0.3033   0.456
## defensive_reverse_Z:as.factor(bias)1   0.9874     0.3025   3.265
##                                                  Pr(>|t|)    
## (Intercept)                          < 0.0000000000000002 ***
## defensive_reverse_Z                               0.00145 ** 
## as.factor(bias)1                                  0.64878    
## defensive_reverse_Z:as.factor(bias)1              0.00139 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.649 on 133 degrees of freedom
##   (181 observations deleted due to missingness)
## Multiple R-squared:  0.08551,    Adjusted R-squared:  0.06488 
## F-statistic: 4.145 on 3 and 133 DF,  p-value: 0.007617
summary(lm(rl_purchase ~ defensive_reverse_Z*as.factor(bias), wf_dt2))
## 
## Call:
## lm(formula = rl_purchase ~ defensive_reverse_Z * as.factor(bias), 
##     data = wf_dt2)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.4990 -1.4093  0.1638  1.1922  3.3408 
## 
## Coefficients:
##                                      Estimate Std. Error t value
## (Intercept)                            3.4183     0.2289  14.936
## defensive_reverse_Z                   -0.8404     0.2476  -3.393
## as.factor(bias)1                       0.4828     0.3030   1.593
## defensive_reverse_Z:as.factor(bias)1   0.5425     0.3140   1.728
##                                                  Pr(>|t|)    
## (Intercept)                          < 0.0000000000000002 ***
## defensive_reverse_Z                              0.000908 ***
## as.factor(bias)1                                 0.113408    
## defensive_reverse_Z:as.factor(bias)1             0.086294 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.724 on 134 degrees of freedom
##   (180 observations deleted due to missingness)
## Multiple R-squared:  0.09779,    Adjusted R-squared:  0.07759 
## F-statistic: 4.842 on 3 and 134 DF,  p-value: 0.00313
summary(lm(rr_purchase ~ defensive_reverse_Z + as.factor(bias), wf_dt2))
## 
## Call:
## lm(formula = rr_purchase ~ defensive_reverse_Z + as.factor(bias), 
##     data = wf_dt2)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.9287 -0.5855  0.0003  1.4145  2.8524 
## 
## Coefficients:
##                     Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)           4.3587     0.2191  19.896 <0.0000000000000002 ***
## defensive_reverse_Z  -0.2016     0.1566  -1.288               0.200    
## as.factor(bias)1      0.1479     0.3140   0.471               0.638    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.708 on 134 degrees of freedom
##   (181 observations deleted due to missingness)
## Multiple R-squared:  0.01222,    Adjusted R-squared:  -0.002519 
## F-statistic: 0.8291 on 2 and 134 DF,  p-value: 0.4387
summary(lm(rl_purchase ~ defensive_reverse_Z + as.factor(bias), wf_dt2))
## 
## Call:
## lm(formula = rl_purchase ~ defensive_reverse_Z + as.factor(bias), 
##     data = wf_dt2)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.9028 -1.5245  0.2586  1.2195  3.4770 
## 
## Coefficients:
##                     Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)           3.5005     0.2255  15.523 < 0.0000000000000002 ***
## defensive_reverse_Z  -0.5028     0.1533  -3.279              0.00132 ** 
## as.factor(bias)1      0.4309     0.3037   1.419              0.15824    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.737 on 135 degrees of freedom
##   (180 observations deleted due to missingness)
## Multiple R-squared:  0.07769,    Adjusted R-squared:  0.06403 
## F-statistic: 5.686 on 2 and 135 DF,  p-value: 0.004258
summary(lm(rr_purchase ~ defensive_reverse_Z, wf_dt2))
## 
## Call:
## lm(formula = rr_purchase ~ defensive_reverse_Z, data = wf_dt2)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.8012 -0.5475 -0.1248  1.3961  2.8752 
## 
## Coefficients:
##                     Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)           4.4357     0.1455  30.479 <0.0000000000000002 ***
## defensive_reverse_Z  -0.1746     0.1453  -1.202               0.231    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.703 on 135 degrees of freedom
##   (181 observations deleted due to missingness)
## Multiple R-squared:  0.01059,    Adjusted R-squared:  0.003259 
## F-statistic: 1.445 on 1 and 135 DF,  p-value: 0.2315
summary(lm(rl_purchase ~ defensive_reverse_Z, wf_dt2))
## 
## Call:
## lm(formula = rl_purchase ~ defensive_reverse_Z, data = wf_dt2)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.6303 -1.5726  0.2232  1.3160  3.6315 
## 
## Coefficients:
##                     Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)           3.7420     0.1485  25.206 < 0.0000000000000002 ***
## defensive_reverse_Z  -0.4599     0.1509  -3.048              0.00277 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.743 on 136 degrees of freedom
##   (180 observations deleted due to missingness)
## Multiple R-squared:  0.06394,    Adjusted R-squared:  0.05705 
## F-statistic: 9.289 on 1 and 136 DF,  p-value: 0.00277
summary(lm(rr_purchase ~ as.factor(bias), wf_dt2))
## 
## Call:
## lm(formula = rr_purchase ~ as.factor(bias), data = wf_dt2)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.5000 -0.5000 -0.4133  1.5000  2.5867 
## 
## Coefficients:
##                  Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)       4.50000    0.20557  21.890 <0.0000000000000002 ***
## as.factor(bias)1 -0.08667    0.28386  -0.305               0.761    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.695 on 141 degrees of freedom
##   (175 observations deleted due to missingness)
## Multiple R-squared:  0.0006607,  Adjusted R-squared:  -0.006427 
## F-statistic: 0.09322 on 1 and 141 DF,  p-value: 0.7606
summary(lm(rl_purchase ~ as.factor(bias), wf_dt2))
## 
## Call:
## lm(formula = rl_purchase ~ as.factor(bias), data = wf_dt2)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.8571 -1.6032  0.1429  1.3968  3.3968 
## 
## Coefficients:
##                  Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)        3.6032     0.2271  15.864 <0.0000000000000002 ***
## as.factor(bias)1   0.2540     0.3063   0.829               0.408    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.803 on 138 degrees of freedom
##   (178 observations deleted due to missingness)
## Multiple R-squared:  0.004958,   Adjusted R-squared:  -0.002252 
## F-statistic: 0.6877 on 1 and 138 DF,  p-value: 0.4084
# Create the scatter plot with a line for the interaction effect
ggplot(wf_dt2, aes(x = defensive_reverse_Z, y = rr_purchase, color = as.factor(bias))) +
  geom_point() +
  geom_smooth(method = "lm", se = FALSE, linetype = "solid", aes(group = as.factor(bias))) +
  labs(title = "Scatter Plot of Defensive Reverse and RR Shop Intentions by Bias",
       x = "Defensive Reverse",
       y = "RR Purchase Intentions",
       color = "Bias") +
  theme_minimal()
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 181 rows containing non-finite values (`stat_smooth()`).
## Warning: Removed 181 rows containing missing values (`geom_point()`).

The bias condition and the brand race condition interact to predict less support to purchase from black brands for people who are biased.This interaction does not hold when accounting for defensiveness. It seems to be the case that defensive responding is what is driving this effect where people who learn they are biased are slightly less support black brands than people who think they are not biased. It does seem like defensiveness in response to bias feedback does make participants more likely than others (no bias feedback) within the black brand condition to purchase from black brands. This is likely a form of morally affirming the self. Note that this pattern does not occur within the RL condition.

Ex predicting purchase_Z:as.factor(brand_race)1 0.35798 0.14791 2.420 0.01617 * defensive_reverse_Z -0.16558 0.05270 -3.142 0.00186 ** as.factor(bias)1:as.factor(brand_race)1 -0.02875 0.20196 -0.142 0.88691

Yet bias feedback does not impact willingness to purchase from a black business alone (i.e., RR_purchase), or in other words people learning that they were biased were not less likely to purchase from a black brand than others who did not learn that they were biased. Although they were more likely to purchase from a white business.

Ex predicting rr_purchase: defensive_reverse_Z -0.6959 0.2140 -3.252 0.00145 ** as.factor(bias)1 0.1384 0.3033 0.456 0.64878
defensive_reverse_Z:as.factor(bias)1 0.9874 0.3025 3.265 0.00139 **

summary(lm(rr_purchase ~ BMIS_shame_2_Z + as.factor(bias), wf_dt2))
## 
## Call:
## lm(formula = rr_purchase ~ BMIS_shame_2_Z + as.factor(bias), 
##     data = wf_dt2)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.5068 -0.5018 -0.4082  1.5031  2.5918 
## 
## Coefficients:
##                   Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)       4.500710   0.207359  21.705 <0.0000000000000002 ***
## BMIS_shame_2_Z    0.004779   0.140676   0.034               0.973    
## as.factor(bias)1 -0.088622   0.290628  -0.305               0.761    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.701 on 140 degrees of freedom
##   (175 observations deleted due to missingness)
## Multiple R-squared:  0.0006689,  Adjusted R-squared:  -0.01361 
## F-statistic: 0.04686 on 2 and 140 DF,  p-value: 0.9542
summary(lm(rr_purchase ~ BMIS_shame_2_Z*as.factor(bias), wf_dt2))
## 
## Call:
## lm(formula = rr_purchase ~ BMIS_shame_2_Z * as.factor(bias), 
##     data = wf_dt2)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.6824 -0.6234 -0.0380  1.3766  2.7669 
## 
## Coefficients:
##                                 Estimate Std. Error t value            Pr(>|t|)
## (Intercept)                       4.4721     0.2088  21.420 <0.0000000000000002
## BMIS_shame_2_Z                   -0.1880     0.2233  -0.842               0.401
## as.factor(bias)1                 -0.0930     0.2904  -0.320               0.749
## BMIS_shame_2_Z:as.factor(bias)1   0.3194     0.2874   1.111               0.268
##                                    
## (Intercept)                     ***
## BMIS_shame_2_Z                     
## as.factor(bias)1                   
## BMIS_shame_2_Z:as.factor(bias)1    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.7 on 139 degrees of freedom
##   (175 observations deleted due to missingness)
## Multiple R-squared:  0.009472,   Adjusted R-squared:  -0.01191 
## F-statistic: 0.443 on 3 and 139 DF,  p-value: 0.7226
summary(lm(purchase_Z ~ BMIS_shame_2_Z*as.factor(brand_race) + as.factor(bias), wf_dt2))
## 
## Call:
## lm(formula = purchase_Z ~ BMIS_shame_2_Z * as.factor(brand_race) + 
##     as.factor(bias), data = wf_dt2)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.0411 -0.5982  0.1311  0.6980  1.7773 
## 
## Coefficients:
##                                       Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                           -0.05009    0.09243  -0.542 0.588327    
## BMIS_shame_2_Z                        -0.10448    0.07629  -1.370 0.171925    
## as.factor(brand_race)1                 0.36433    0.10030   3.632 0.000334 ***
## as.factor(bias)1                       0.09846    0.10447   0.943 0.346746    
## BMIS_shame_2_Z:as.factor(brand_race)1  0.10496    0.10100   1.039 0.299623    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8439 on 280 degrees of freedom
##   (33 observations deleted due to missingness)
## Multiple R-squared:  0.05048,    Adjusted R-squared:  0.03691 
## F-statistic: 3.721 on 4 and 280 DF,  p-value: 0.005721
summary(lm(rl_purchase ~ BMIS_shame_2_Z*as.factor(bias), wf_dt2))
## 
## Call:
## lm(formula = rl_purchase ~ BMIS_shame_2_Z * as.factor(bias), 
##     data = wf_dt2)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.0819 -1.4163  0.1458  1.1458  3.5837 
## 
## Coefficients:
##                                 Estimate Std. Error t value            Pr(>|t|)
## (Intercept)                       3.2487     0.2759  11.775 <0.0000000000000002
## BMIS_shame_2_Z                   -0.7524     0.3783  -1.989              0.0487
## as.factor(bias)1                  0.6612     0.3452   1.915              0.0576
## BMIS_shame_2_Z:as.factor(bias)1   0.5386     0.4214   1.278              0.2034
##                                    
## (Intercept)                     ***
## BMIS_shame_2_Z                  *  
## as.factor(bias)1                .  
## BMIS_shame_2_Z:as.factor(bias)1    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.776 on 135 degrees of freedom
##   (179 observations deleted due to missingness)
## Multiple R-squared:  0.044,  Adjusted R-squared:  0.02276 
## F-statistic: 2.071 on 3 and 135 DF,  p-value: 0.107
summary(lm(rr_purchase ~ BMIS_shame_2_Z + as.factor(bias), wf_dt2))
## 
## Call:
## lm(formula = rr_purchase ~ BMIS_shame_2_Z + as.factor(bias), 
##     data = wf_dt2)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.5068 -0.5018 -0.4082  1.5031  2.5918 
## 
## Coefficients:
##                   Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)       4.500710   0.207359  21.705 <0.0000000000000002 ***
## BMIS_shame_2_Z    0.004779   0.140676   0.034               0.973    
## as.factor(bias)1 -0.088622   0.290628  -0.305               0.761    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.701 on 140 degrees of freedom
##   (175 observations deleted due to missingness)
## Multiple R-squared:  0.0006689,  Adjusted R-squared:  -0.01361 
## F-statistic: 0.04686 on 2 and 140 DF,  p-value: 0.9542
summary(lm(rl_purchase ~ BMIS_shame_2_Z + as.factor(bias), wf_dt2))
## 
## Call:
## lm(formula = rl_purchase ~ BMIS_shame_2_Z + as.factor(bias), 
##     data = wf_dt2)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.1918 -1.5310  0.3129  1.3129  3.7993 
## 
## Coefficients:
##                  Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)        3.4309     0.2368  14.491 <0.0000000000000002 ***
## BMIS_shame_2_Z    -0.3183     0.1671  -1.905              0.0588 .  
## as.factor(bias)1   0.5047     0.3236   1.560              0.1211    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.781 on 136 degrees of freedom
##   (179 observations deleted due to missingness)
## Multiple R-squared:  0.03243,    Adjusted R-squared:  0.0182 
## F-statistic: 2.279 on 2 and 136 DF,  p-value: 0.1063
summary(lm(rr_purchase ~ BMIS_shame_2_Z, wf_dt2))
## 
## Call:
## lm(formula = rr_purchase ~ BMIS_shame_2_Z, data = wf_dt2)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.4578 -0.4578 -0.4462  1.5422  2.5538 
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)     4.454791   0.142096  31.351 <0.0000000000000002 ***
## BMIS_shame_2_Z -0.003719   0.137444  -0.027               0.978    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.696 on 141 degrees of freedom
##   (175 observations deleted due to missingness)
## Multiple R-squared:  5.193e-06,  Adjusted R-squared:  -0.007087 
## F-statistic: 0.0007322 on 1 and 141 DF,  p-value: 0.9785
summary(lm(rl_purchase ~ BMIS_shame_2_Z, wf_dt2))
## 
## Call:
## lm(formula = rl_purchase ~ BMIS_shame_2_Z, data = wf_dt2)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.8991 -1.5431  0.1009  1.3383  3.8129 
## 
## Coefficients:
##                Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)      3.7150     0.1520   24.44 <0.0000000000000002 ***
## BMIS_shame_2_Z  -0.2287     0.1577   -1.45               0.149    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.79 on 137 degrees of freedom
##   (179 observations deleted due to missingness)
## Multiple R-squared:  0.01512,    Adjusted R-squared:  0.007931 
## F-statistic: 2.103 on 1 and 137 DF,  p-value: 0.1493

Shame does not seem to be related to purchase behavior at all.

summary(lm(rr_purchase ~ shame_discrepancy + as.factor(bias), wf_dt2))
## 
## Call:
## lm(formula = rr_purchase ~ shame_discrepancy + as.factor(bias), 
##     data = wf_dt2)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.8296 -0.5473 -0.0748  1.4527  2.7246 
## 
## Coefficients:
##                   Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)         4.5697     0.2133  21.428 <0.0000000000000002 ***
## shame_discrepancy   0.2326     0.1932   1.204               0.231    
## as.factor(bias)1   -0.2005     0.2988  -0.671               0.503    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.692 on 140 degrees of freedom
##   (175 observations deleted due to missingness)
## Multiple R-squared:  0.0109, Adjusted R-squared:  -0.00323 
## F-statistic: 0.7714 on 2 and 140 DF,  p-value: 0.4643
summary(lm(rr_purchase ~ shame_discrepancy*as.factor(bias), wf_dt2))
## 
## Call:
## lm(formula = rr_purchase ~ shame_discrepancy * as.factor(bias), 
##     data = wf_dt2)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.0276 -0.5163 -0.0276  1.4847  2.7299 
## 
## Coefficients:
##                                    Estimate Std. Error t value
## (Intercept)                          4.4953     0.2305  19.506
## shame_discrepancy                   -0.0158     0.3484  -0.045
## as.factor(bias)1                    -0.1470     0.3055  -0.481
## shame_discrepancy:as.factor(bias)1   0.3590     0.4188   0.857
##                                               Pr(>|t|)    
## (Intercept)                        <0.0000000000000002 ***
## shame_discrepancy                                0.964    
## as.factor(bias)1                                 0.631    
## shame_discrepancy:as.factor(bias)1               0.393    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.694 on 139 degrees of freedom
##   (175 observations deleted due to missingness)
## Multiple R-squared:  0.0161, Adjusted R-squared:  -0.005136 
## F-statistic: 0.7582 on 3 and 139 DF,  p-value: 0.5194
summary(lm(purchase_Z ~ shame_discrepancy*as.factor(brand_race) + as.factor(bias), wf_dt2))
## 
## Call:
## lm(formula = purchase_Z ~ shame_discrepancy * as.factor(brand_race) + 
##     as.factor(bias), data = wf_dt2)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.0829 -0.7424  0.1644  0.6622  1.5985 
## 
## Coefficients:
##                                           Estimate Std. Error t value Pr(>|t|)
## (Intercept)                               0.001540   0.091944   0.017 0.986651
## shame_discrepancy                         0.112011   0.095606   1.172 0.242361
## as.factor(brand_race)1                    0.362735   0.100071   3.625 0.000343
## as.factor(bias)1                          0.009943   0.106964   0.093 0.926002
## shame_discrepancy:as.factor(brand_race)1 -0.022127   0.129677  -0.171 0.864635
##                                             
## (Intercept)                                 
## shame_discrepancy                           
## as.factor(brand_race)1                   ***
## as.factor(bias)1                            
## shame_discrepancy:as.factor(brand_race)1    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8435 on 280 degrees of freedom
##   (33 observations deleted due to missingness)
## Multiple R-squared:  0.0514, Adjusted R-squared:  0.03785 
## F-statistic: 3.793 on 4 and 280 DF,  p-value: 0.00507
summary(lm(rl_purchase ~ shame_discrepancy*as.factor(bias), wf_dt2))
## 
## Call:
## lm(formula = rl_purchase ~ shame_discrepancy * as.factor(bias), 
##     data = wf_dt2)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.9697 -1.5812  0.2311  1.4188  3.4188 
## 
## Coefficients:
##                                    Estimate Std. Error t value
## (Intercept)                         3.58921    0.26089  13.758
## shame_discrepancy                   0.08345    0.42078   0.198
## as.factor(bias)1                    0.20098    0.34052   0.590
## shame_discrepancy:as.factor(bias)1  0.13819    0.48794   0.283
##                                               Pr(>|t|)    
## (Intercept)                        <0.0000000000000002 ***
## shame_discrepancy                                0.843    
## as.factor(bias)1                                 0.556    
## shame_discrepancy:as.factor(bias)1               0.777    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.805 on 135 degrees of freedom
##   (179 observations deleted due to missingness)
## Multiple R-squared:  0.01277,    Adjusted R-squared:  -0.009165 
## F-statistic: 0.5823 on 3 and 135 DF,  p-value: 0.6276
summary(lm(rr_purchase ~ shame_discrepancy + as.factor(bias), wf_dt2))
## 
## Call:
## lm(formula = rr_purchase ~ shame_discrepancy + as.factor(bias), 
##     data = wf_dt2)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.8296 -0.5473 -0.0748  1.4527  2.7246 
## 
## Coefficients:
##                   Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)         4.5697     0.2133  21.428 <0.0000000000000002 ***
## shame_discrepancy   0.2326     0.1932   1.204               0.231    
## as.factor(bias)1   -0.2005     0.2988  -0.671               0.503    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.692 on 140 degrees of freedom
##   (175 observations deleted due to missingness)
## Multiple R-squared:  0.0109, Adjusted R-squared:  -0.00323 
## F-statistic: 0.7714 on 2 and 140 DF,  p-value: 0.4643
summary(lm(rl_purchase ~ shame_discrepancy + as.factor(bias), wf_dt2))
## 
## Call:
## lm(formula = rl_purchase ~ shame_discrepancy + as.factor(bias), 
##     data = wf_dt2)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -2.952 -1.602  0.217  1.398  3.398 
## 
## Coefficients:
##                   Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)         3.6196     0.2370  15.275 <0.0000000000000002 ***
## shame_discrepancy   0.1862     0.2123   0.877               0.382    
## as.factor(bias)1    0.1813     0.3322   0.546               0.586    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.799 on 136 degrees of freedom
##   (179 observations deleted due to missingness)
## Multiple R-squared:  0.01219,    Adjusted R-squared:  -0.00234 
## F-statistic: 0.8389 on 2 and 136 DF,  p-value: 0.4344
summary(lm(rr_purchase ~ shame_discrepancy, wf_dt2))
## 
## Call:
## lm(formula = rr_purchase ~ shame_discrepancy, data = wf_dt2)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.8420 -0.4444 -0.1952  1.5556  2.7796 
## 
## Coefficients:
##                   Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)         4.4628     0.1415  31.544 <0.0000000000000002 ***
## shame_discrepancy   0.1915     0.1829   1.047               0.297    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.689 on 141 degrees of freedom
##   (175 observations deleted due to missingness)
## Multiple R-squared:  0.007718,   Adjusted R-squared:  0.0006803 
## F-statistic: 1.097 on 1 and 141 DF,  p-value: 0.2968
summary(lm(rl_purchase ~ shame_discrepancy, wf_dt2))
## 
## Call:
## lm(formula = rl_purchase ~ shame_discrepancy, data = wf_dt2)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.9052 -1.6811  0.3037  1.3037  3.3340 
## 
## Coefficients:
##                   Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)         3.7185     0.1524  24.406 <0.0000000000000002 ***
## shame_discrepancy   0.2305     0.1957   1.178               0.241    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.794 on 137 degrees of freedom
##   (179 observations deleted due to missingness)
## Multiple R-squared:  0.01002,    Adjusted R-squared:  0.002798 
## F-statistic: 1.387 on 1 and 137 DF,  p-value: 0.2409
summary(lm(rr_purchase ~ bias_aware_Z + as.factor(bias), wf_dt2))
## 
## Call:
## lm(formula = rr_purchase ~ bias_aware_Z + as.factor(bias), data = wf_dt2)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.6200 -0.5586 -0.2833  1.5194  2.6877 
## 
## Coefficients:
##                  Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)       4.48586    0.20759  21.609 <0.0000000000000002 ***
## bias_aware_Z     -0.08485    0.15050  -0.564               0.574    
## as.factor(bias)1 -0.07584    0.28519  -0.266               0.791    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.699 on 140 degrees of freedom
##   (175 observations deleted due to missingness)
## Multiple R-squared:  0.002924,   Adjusted R-squared:  -0.01132 
## F-statistic: 0.2053 on 2 and 140 DF,  p-value: 0.8147
summary(lm(rr_purchase ~ bias_aware_Z*as.factor(bias), wf_dt2))
## 
## Call:
## lm(formula = rr_purchase ~ bias_aware_Z * as.factor(bias), data = wf_dt2)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.6510 -0.5174 -0.2006  1.4970  2.7519 
## 
## Coefficients:
##                               Estimate Std. Error t value            Pr(>|t|)
## (Intercept)                    4.49767    0.21020  21.397 <0.0000000000000002
## bias_aware_Z                  -0.01396    0.22967  -0.061               0.952
## as.factor(bias)1              -0.08975    0.28806  -0.312               0.756
## bias_aware_Z:as.factor(bias)1 -0.12481    0.30474  -0.410               0.683
##                                  
## (Intercept)                   ***
## bias_aware_Z                     
## as.factor(bias)1                 
## bias_aware_Z:as.factor(bias)1    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.704 on 139 degrees of freedom
##   (175 observations deleted due to missingness)
## Multiple R-squared:  0.004126,   Adjusted R-squared:  -0.01737 
## F-statistic: 0.192 on 3 and 139 DF,  p-value: 0.9017
summary(lm(purchase_Z ~ bias_aware_Z*as.factor(brand_race)*as.factor(bias), wf_dt2))
## 
## Call:
## lm(formula = purchase_Z ~ bias_aware_Z * as.factor(brand_race) * 
##     as.factor(bias), data = wf_dt2)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.0752 -0.8039  0.1340  0.7867  1.7780 
## 
## Coefficients:
##                                                       Estimate Std. Error
## (Intercept)                                          -0.192968   0.111792
## bias_aware_Z                                         -0.001280   0.109804
## as.factor(brand_race)1                                0.408333   0.156454
## as.factor(bias)1                                      0.222477   0.153118
## bias_aware_Z:as.factor(brand_race)1                  -0.108982   0.163828
## bias_aware_Z:as.factor(bias)1                        -0.002967   0.146318
## as.factor(brand_race)1:as.factor(bias)1              -0.159722   0.215868
## bias_aware_Z:as.factor(brand_race)1:as.factor(bias)1  0.063702   0.218724
##                                                      t value Pr(>|t|)   
## (Intercept)                                           -1.726  0.08537 . 
## bias_aware_Z                                          -0.012  0.99070   
## as.factor(brand_race)1                                 2.610  0.00952 **
## as.factor(bias)1                                       1.453  0.14730   
## bias_aware_Z:as.factor(brand_race)1                   -0.665  0.50643   
## bias_aware_Z:as.factor(bias)1                         -0.020  0.98384   
## as.factor(brand_race)1:as.factor(bias)1               -0.740  0.45995   
## bias_aware_Z:as.factor(brand_race)1:as.factor(bias)1   0.291  0.77107   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9325 on 294 degrees of freedom
##   (16 observations deleted due to missingness)
## Multiple R-squared:  0.04148,    Adjusted R-squared:  0.01866 
## F-statistic: 1.817 on 7 and 294 DF,  p-value: 0.08359
summary(lm(rl_purchase ~ bias_aware_Z*as.factor(bias), wf_dt2))
## 
## Call:
## lm(formula = rl_purchase ~ bias_aware_Z * as.factor(bias), data = wf_dt2)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.0493 -1.6807  0.1642  1.2466  3.4411 
## 
## Coefficients:
##                               Estimate Std. Error t value            Pr(>|t|)
## (Intercept)                    3.61735    0.22971  15.748 <0.0000000000000002
## bias_aware_Z                  -0.12471    0.22353  -0.558               0.578
## as.factor(bias)1               0.25195    0.30974   0.813               0.417
## bias_aware_Z:as.factor(bias)1  0.02197    0.29586   0.074               0.941
##                                  
## (Intercept)                   ***
## bias_aware_Z                     
## as.factor(bias)1                 
## bias_aware_Z:as.factor(bias)1    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.812 on 136 degrees of freedom
##   (178 observations deleted due to missingness)
## Multiple R-squared:  0.009273,   Adjusted R-squared:  -0.01258 
## F-statistic: 0.4243 on 3 and 136 DF,  p-value: 0.7359
summary(lm(rr_purchase ~ bias_aware_Z + as.factor(bias), wf_dt2))
## 
## Call:
## lm(formula = rr_purchase ~ bias_aware_Z + as.factor(bias), data = wf_dt2)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.6200 -0.5586 -0.2833  1.5194  2.6877 
## 
## Coefficients:
##                  Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)       4.48586    0.20759  21.609 <0.0000000000000002 ***
## bias_aware_Z     -0.08485    0.15050  -0.564               0.574    
## as.factor(bias)1 -0.07584    0.28519  -0.266               0.791    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.699 on 140 degrees of freedom
##   (175 observations deleted due to missingness)
## Multiple R-squared:  0.002924,   Adjusted R-squared:  -0.01132 
## F-statistic: 0.2053 on 2 and 140 DF,  p-value: 0.8147
summary(lm(rl_purchase ~ bias_aware_Z + as.factor(bias), wf_dt2))
## 
## Call:
## lm(formula = rl_purchase ~ bias_aware_Z + as.factor(bias), data = wf_dt2)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.0669 -1.6646  0.1876  1.2554  3.4367 
## 
## Coefficients:
##                  Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)        3.6159     0.2281  15.854 <0.0000000000000002 ***
## bias_aware_Z      -0.1122     0.1459  -0.769               0.443    
## as.factor(bias)1   0.2545     0.3067   0.830               0.408    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.805 on 137 degrees of freedom
##   (178 observations deleted due to missingness)
## Multiple R-squared:  0.009233,   Adjusted R-squared:  -0.005231 
## F-statistic: 0.6383 on 2 and 137 DF,  p-value: 0.5297
summary(lm(rr_purchase ~ bias_aware_Z, wf_dt2))
## 
## Call:
## lm(formula = rr_purchase ~ bias_aware_Z, data = wf_dt2)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.5992 -0.5393 -0.3001  1.5055  2.6550 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)   4.44582    0.14242  31.217 <0.0000000000000002 ***
## bias_aware_Z -0.08754    0.14967  -0.585                0.56    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.694 on 141 degrees of freedom
##   (175 observations deleted due to missingness)
## Multiple R-squared:  0.002421,   Adjusted R-squared:  -0.004654 
## F-statistic: 0.3421 on 1 and 141 DF,  p-value: 0.5595
summary(lm(rl_purchase ~ bias_aware_Z, wf_dt2))
## 
## Call:
## lm(formula = rl_purchase ~ bias_aware_Z, data = wf_dt2)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.9519 -1.6126  0.2106  1.2775  3.5068 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)    3.7559     0.1534  24.491 <0.0000000000000002 ***
## bias_aware_Z  -0.1119     0.1457  -0.768               0.444    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.803 on 138 degrees of freedom
##   (178 observations deleted due to missingness)
## Multiple R-squared:  0.004254,   Adjusted R-squared:  -0.002962 
## F-statistic: 0.5895 on 1 and 138 DF,  p-value: 0.4439

Bias awareness does not predict anything.

6. Shop Intentions

summary(lm(shop_intentions_Z ~ as.factor(bias)*as.factor(brand_race), wf_dt2)) # main interaction working, it is defensiveness driving this effect, whe defensiveness is included as a term in this model the effect vanishes, but defensiveness on its own is still negatively correlated with the dv, it also loses its sig for shame and se, but they are not signifgicant predictors of the dv
## 
## Call:
## lm(formula = shop_intentions_Z ~ as.factor(bias) * as.factor(brand_race), 
##     data = wf_dt2)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2.03843 -0.86980  0.06421  0.76362  1.92776 
## 
## Coefficients:
##                                         Estimate Std. Error t value   Pr(>|t|)
## (Intercept)                              -0.4460     0.1038  -4.297 0.00002310
## as.factor(bias)1                          0.4625     0.1495   3.094    0.00215
## as.factor(brand_race)1                    0.6971     0.1526   4.568 0.00000707
## as.factor(bias)1:as.factor(brand_race)1  -0.4773     0.2162  -2.208    0.02800
##                                            
## (Intercept)                             ***
## as.factor(bias)1                        ** 
## as.factor(brand_race)1                  ***
## as.factor(bias)1:as.factor(brand_race)1 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9624 on 314 degrees of freedom
## Multiple R-squared:  0.08251,    Adjusted R-squared:  0.07374 
## F-statistic: 9.412 on 3 and 314 DF,  p-value: 0.000005659
summary(lm(shop_intentions ~ shame_discrepancy , wf_dt2)) # increases in shame from t1 to t2 are associated with increases in shopping attentions, this effect is for across all of the conditions 
## 
## Call:
## lm(formula = shop_intentions ~ shame_discrepancy, data = wf_dt2)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.2249 -1.2249 -0.1547  1.2210  3.3995 
## 
## Coefficients:
##                   Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)         4.2505     0.1066  39.858 <0.0000000000000002 ***
## shame_discrepancy   0.2670     0.1381   1.932              0.0543 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.8 on 283 degrees of freedom
##   (33 observations deleted due to missingness)
## Multiple R-squared:  0.01302,    Adjusted R-squared:  0.009537 
## F-statistic: 3.734 on 1 and 283 DF,  p-value: 0.0543
summary(lm(shop_intentions_Z ~ as.factor(bias)*as.factor(brand_race) + defensive_reverse_Z, wf_dt2))
## 
## Call:
## lm(formula = shop_intentions_Z ~ as.factor(bias) * as.factor(brand_race) + 
##     defensive_reverse_Z, data = wf_dt2)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.0582 -0.4291  0.1117  0.5622  1.5260 
## 
## Coefficients:
##                                         Estimate Std. Error t value Pr(>|t|)   
## (Intercept)                             -0.02147    0.10528  -0.204  0.83860   
## as.factor(bias)1                         0.11232    0.14165   0.793  0.42850   
## as.factor(brand_race)1                   0.34435    0.14562   2.365  0.01874 * 
## defensive_reverse_Z                     -0.16625    0.05188  -3.204  0.00151 **
## as.factor(bias)1:as.factor(brand_race)1 -0.04522    0.19882  -0.227  0.82023   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8226 on 273 degrees of freedom
##   (40 observations deleted due to missingness)
## Multiple R-squared:  0.06969,    Adjusted R-squared:  0.05606 
## F-statistic: 5.113 on 4 and 273 DF,  p-value: 0.0005487
summary(lm(shop_intentions_Z ~ defensive_reverse_Z*as.factor(bias), wf_dt2))
## 
## Call:
## lm(formula = shop_intentions_Z ~ defensive_reverse_Z * as.factor(bias), 
##     data = wf_dt2)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.97049 -0.48680  0.00777  0.55890  1.65826 
## 
## Coefficients:
##                                      Estimate Std. Error t value  Pr(>|t|)    
## (Intercept)                           0.10089    0.07684   1.313   0.19032    
## defensive_reverse_Z                  -0.34146    0.07854  -4.348 0.0000194 ***
## as.factor(bias)1                      0.09565    0.10374   0.922   0.35734    
## defensive_reverse_Z:as.factor(bias)1  0.31618    0.10433   3.031   0.00267 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8231 on 274 degrees of freedom
##   (40 observations deleted due to missingness)
## Multiple R-squared:  0.06508,    Adjusted R-squared:  0.05485 
## F-statistic: 6.358 on 3 and 274 DF,  p-value: 0.0003513
summary(lm(shop_intentions_Z ~ defensive_reverse_Z*as.factor(bias)*as.factor(brand_race), wf_dt2))
## 
## Call:
## lm(formula = shop_intentions_Z ~ defensive_reverse_Z * as.factor(bias) * 
##     as.factor(brand_race), data = wf_dt2)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.1018 -0.4540  0.1415  0.5392  1.5492 
## 
## Coefficients:
##                                                             Estimate Std. Error
## (Intercept)                                                 -0.06561    0.10702
## defensive_reverse_Z                                         -0.34106    0.11603
## as.factor(bias)1                                             0.15278    0.14178
## as.factor(brand_race)1                                       0.33061    0.15140
## defensive_reverse_Z:as.factor(bias)1                         0.19666    0.14639
## defensive_reverse_Z:as.factor(brand_race)1                   0.01997    0.15592
## as.factor(bias)1:as.factor(brand_race)1                     -0.12989    0.20559
## defensive_reverse_Z:as.factor(bias)1:as.factor(brand_race)1  0.22875    0.20811
##                                                             t value Pr(>|t|)   
## (Intercept)                                                  -0.613  0.54038   
## defensive_reverse_Z                                          -2.939  0.00357 **
## as.factor(bias)1                                              1.078  0.28220   
## as.factor(brand_race)1                                        2.184  0.02985 * 
## defensive_reverse_Z:as.factor(bias)1                          1.343  0.18029   
## defensive_reverse_Z:as.factor(brand_race)1                    0.128  0.89818   
## as.factor(bias)1:as.factor(brand_race)1                      -0.632  0.52805   
## defensive_reverse_Z:as.factor(bias)1:as.factor(brand_race)1   1.099  0.27268   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8105 on 270 degrees of freedom
##   (40 observations deleted due to missingness)
## Multiple R-squared:  0.1067, Adjusted R-squared:  0.0835 
## F-statistic: 4.605 on 7 and 270 DF,  p-value: 0.00006965
summary(lm(shop_intentions_Z ~ defensive_reverse_Z + as.factor(brand_race), wf_dt2))
## 
## Call:
## lm(formula = shop_intentions_Z ~ defensive_reverse_Z + as.factor(brand_race), 
##     data = wf_dt2)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2.00042 -0.45018  0.09071  0.57601  1.56561 
## 
## Coefficients:
##                        Estimate Std. Error t value Pr(>|t|)   
## (Intercept)             0.04133    0.06937   0.596  0.55182   
## defensive_reverse_Z    -0.15408    0.04932  -3.124  0.00198 **
## as.factor(brand_race)1  0.31634    0.09847   3.212  0.00147 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8208 on 275 degrees of freedom
##   (40 observations deleted due to missingness)
## Multiple R-squared:  0.06692,    Adjusted R-squared:  0.06014 
## F-statistic: 9.862 on 2 and 275 DF,  p-value: 0.00007307
summary(lm(shop_intentions_Z ~ defensive_reverse_Z*as.factor(brand_race), wf_dt2))
## 
## Call:
## lm(formula = shop_intentions_Z ~ defensive_reverse_Z * as.factor(brand_race), 
##     data = wf_dt2)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.8980 -0.3948  0.1070  0.6027  1.6065 
## 
## Coefficients:
##                                            Estimate Std. Error t value Pr(>|t|)
## (Intercept)                                 0.04045    0.06938   0.583  0.56035
## defensive_reverse_Z                        -0.20333    0.07003  -2.903  0.00399
## as.factor(brand_race)1                      0.31634    0.09848   3.212  0.00147
## defensive_reverse_Z:as.factor(brand_race)1  0.09775    0.09865   0.991  0.32265
##                                              
## (Intercept)                                  
## defensive_reverse_Z                        **
## as.factor(brand_race)1                     **
## defensive_reverse_Z:as.factor(brand_race)1   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8208 on 274 degrees of freedom
##   (40 observations deleted due to missingness)
## Multiple R-squared:  0.07025,    Adjusted R-squared:  0.06007 
## F-statistic: 6.901 on 3 and 274 DF,  p-value: 0.0001701
summary(lm(rr_shop_intentions ~ defensive_reverse_Z + as.factor(bias), wf_dt2))
## 
## Call:
## lm(formula = rr_shop_intentions ~ defensive_reverse_Z + as.factor(bias), 
##     data = wf_dt2)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.1108 -0.6707  0.2284  1.3463  2.8034 
## 
## Coefficients:
##                     Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)          4.56328    0.21883   20.85 <0.0000000000000002 ***
## defensive_reverse_Z -0.23602    0.15633   -1.51               0.133    
## as.factor(bias)1     0.05356    0.31468    0.17               0.865    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.713 on 135 degrees of freedom
##   (180 observations deleted due to missingness)
## Multiple R-squared:  0.01789,    Adjusted R-squared:  0.00334 
## F-statistic:  1.23 on 2 and 135 DF,  p-value: 0.2957
summary(lm(rr_shop_intentions ~ defensive_reverse_Z*as.factor(bias), wf_dt2))
## 
## Call:
## lm(formula = rr_shop_intentions ~ defensive_reverse_Z * as.factor(bias), 
##     data = wf_dt2)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.5006 -0.7331  0.2762  1.2148  3.1640 
## 
## Coefficients:
##                                      Estimate Std. Error t value
## (Intercept)                           4.39448    0.21995  19.979
## defensive_reverse_Z                  -0.68755    0.21389  -3.214
## as.factor(bias)1                      0.04901    0.30577   0.160
## defensive_reverse_Z:as.factor(bias)1  0.91092    0.30380   2.998
##                                                  Pr(>|t|)    
## (Intercept)                          < 0.0000000000000002 ***
## defensive_reverse_Z                               0.00164 ** 
## as.factor(bias)1                                  0.87291    
## defensive_reverse_Z:as.factor(bias)1              0.00324 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.665 on 134 degrees of freedom
##   (180 observations deleted due to missingness)
## Multiple R-squared:  0.07964,    Adjusted R-squared:  0.05903 
## F-statistic: 3.865 on 3 and 134 DF,  p-value: 0.01088
summary(lm(rl_shop_intentions ~ defensive_reverse_Z*as.factor(bias), wf_dt2))
## 
## Call:
## lm(formula = rl_shop_intentions ~ defensive_reverse_Z * as.factor(bias), 
##     data = wf_dt2)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.6258 -1.2417  0.2224  1.0750  3.3173 
## 
## Coefficients:
##                                      Estimate Std. Error t value
## (Intercept)                            3.6866     0.2355  15.655
## defensive_reverse_Z                   -0.7303     0.2553  -2.860
## as.factor(bias)1                       0.3616     0.3125   1.157
## defensive_reverse_Z:as.factor(bias)1   0.4919     0.3241   1.518
##                                                 Pr(>|t|)    
## (Intercept)                          <0.0000000000000002 ***
## defensive_reverse_Z                               0.0049 ** 
## as.factor(bias)1                                  0.2492    
## defensive_reverse_Z:as.factor(bias)1              0.1314    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.783 on 135 degrees of freedom
##   (179 observations deleted due to missingness)
## Multiple R-squared:  0.06784,    Adjusted R-squared:  0.04713 
## F-statistic: 3.275 on 3 and 135 DF,  p-value: 0.02313
summary(lm(rr_shop_intentions ~ defensive_reverse_Z + as.factor(bias), wf_dt2))
## 
## Call:
## lm(formula = rr_shop_intentions ~ defensive_reverse_Z + as.factor(bias), 
##     data = wf_dt2)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.1108 -0.6707  0.2284  1.3463  2.8034 
## 
## Coefficients:
##                     Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)          4.56328    0.21883   20.85 <0.0000000000000002 ***
## defensive_reverse_Z -0.23602    0.15633   -1.51               0.133    
## as.factor(bias)1     0.05356    0.31468    0.17               0.865    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.713 on 135 degrees of freedom
##   (180 observations deleted due to missingness)
## Multiple R-squared:  0.01789,    Adjusted R-squared:  0.00334 
## F-statistic:  1.23 on 2 and 135 DF,  p-value: 0.2957
summary(lm(rl_shop_intentions ~ defensive_reverse_Z + as.factor(bias), wf_dt2))
## 
## Call:
## lm(formula = rl_shop_intentions ~ defensive_reverse_Z + as.factor(bias), 
##     data = wf_dt2)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.6909 -1.3344  0.3758  1.0825  3.2694 
## 
## Coefficients:
##                     Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)           3.7637     0.2311   16.29 < 0.0000000000000002 ***
## defensive_reverse_Z  -0.4250     0.1580   -2.69              0.00805 ** 
## as.factor(bias)1      0.3121     0.3122    1.00              0.31926    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.792 on 136 degrees of freedom
##   (179 observations deleted due to missingness)
## Multiple R-squared:  0.05194,    Adjusted R-squared:  0.03799 
## F-statistic: 3.725 on 2 and 136 DF,  p-value: 0.02661
summary(lm(rr_shop_intentions ~ defensive_reverse_Z, wf_dt2))
## 
## Call:
## lm(formula = rr_shop_intentions ~ defensive_reverse_Z, data = wf_dt2)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.0643 -0.6902  0.2277  1.3736  2.8115 
## 
## Coefficients:
##                     Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)           4.5910     0.1453  31.588 <0.0000000000000002 ***
## defensive_reverse_Z  -0.2261     0.1445  -1.564                0.12    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.707 on 136 degrees of freedom
##   (180 observations deleted due to missingness)
## Multiple R-squared:  0.01768,    Adjusted R-squared:  0.01046 
## F-statistic: 2.448 on 1 and 136 DF,  p-value: 0.12
summary(lm(rl_shop_intentions ~ defensive_reverse_Z, wf_dt2))
## 
## Call:
## lm(formula = rl_shop_intentions ~ defensive_reverse_Z, data = wf_dt2)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.5064 -1.3403  0.3817  1.1597  3.3817 
## 
## Coefficients:
##                     Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)           3.9375     0.1521   25.89 <0.0000000000000002 ***
## defensive_reverse_Z  -0.3930     0.1547   -2.54              0.0122 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.792 on 137 degrees of freedom
##   (179 observations deleted due to missingness)
## Multiple R-squared:  0.04497,    Adjusted R-squared:  0.038 
## F-statistic: 6.451 on 1 and 137 DF,  p-value: 0.01221
summary(lm(rr_shop_intentions ~ as.factor(bias), wf_dt2))
## 
## Call:
## lm(formula = rr_shop_intentions ~ as.factor(bias), data = wf_dt2)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.6812 -0.6812  0.3188  1.3188  2.4933 
## 
## Coefficients:
##                  Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)        4.6812     0.2045  22.891 <0.0000000000000002 ***
## as.factor(bias)1  -0.1745     0.2834  -0.616               0.539    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.699 on 142 degrees of freedom
##   (174 observations deleted due to missingness)
## Multiple R-squared:  0.002663,   Adjusted R-squared:  -0.00436 
## F-statistic: 0.3792 on 1 and 142 DF,  p-value: 0.539
summary(lm(rl_shop_intentions ~ as.factor(bias), wf_dt2))
## 
## Call:
## lm(formula = rl_shop_intentions ~ as.factor(bias), data = wf_dt2)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.0130 -1.8594  0.1406  1.1406  3.1406 
## 
## Coefficients:
##                  Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)        3.8594     0.2305  16.742 <0.0000000000000002 ***
## as.factor(bias)1   0.1536     0.3119   0.492               0.623    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.844 on 139 degrees of freedom
##   (177 observations deleted due to missingness)
## Multiple R-squared:  0.001742,   Adjusted R-squared:  -0.00544 
## F-statistic: 0.2425 on 1 and 139 DF,  p-value: 0.6232
# Create an interaction plot
interaction.plot(
  x.factor = wf_dt2$brand_race,
  trace.factor = wf_dt2$bias,
  response = wf_dt2$shop_intentions_Z,
  type = "b", col = c("red", "blue"), pch = c(1, 2),
  main = "Interaction Plot of Bias and Brand Race on Shop Intentions",
  xlab = "Brand race",
  ylab = "Shop Intentions",
  legend = TRUE
)

# Create the scatter plot with a line for the interaction effect
ggplot(wf_dt2, aes(x = defensive_reverse_Z, y = shop_intentions_Z, color = as.factor(bias))) +
  geom_point() +
  geom_smooth(method = "lm", se = FALSE, linetype = "solid", aes(group = as.factor(bias))) +
  labs(title = "Scatter Plot of Defensive Reverse and RR Shop Intentions by Bias",
       x = "Defensive Reverse",
       y = "RR Shop Intentions",
       color = "Bias") +
  theme_minimal()
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 40 rows containing non-finite values (`stat_smooth()`).
## Warning: Removed 40 rows containing missing values (`geom_point()`).

Similar to purchase intentions, the main effect is being driven by defensiveness, those who learned that they are biased are less likely to purchase from a black brand than non-biased people, but when you account for this effect it vanishes. What you see when you examine the relationship between defensiveness and bias feedback is that those who are in the bias feedback condition are do not change in their shopping intentions the more defensive they are, while those in the no bias condition become more defensive.

summary(lm(rr_shop_intentions ~ BMIS_shame_2_Z + as.factor(bias), wf_dt2))
## 
## Call:
## lm(formula = rr_shop_intentions ~ BMIS_shame_2_Z + as.factor(bias), 
##     data = wf_dt2)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.6871 -0.6776  0.3129  1.3153  2.5122 
## 
## Coefficients:
##                   Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)       4.679702   0.206417  22.671 <0.0000000000000002 ***
## BMIS_shame_2_Z   -0.009222   0.140755  -0.066               0.948    
## as.factor(bias)1 -0.170631   0.290400  -0.588               0.558    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.705 on 141 degrees of freedom
##   (174 observations deleted due to missingness)
## Multiple R-squared:  0.002694,   Adjusted R-squared:  -0.01145 
## F-statistic: 0.1904 on 2 and 141 DF,  p-value: 0.8268
summary(lm(rr_shop_intentions ~ BMIS_shame_2_Z*as.factor(bias) + defensive_reverse_Z, wf_dt2))
## 
## Call:
## lm(formula = rr_shop_intentions ~ BMIS_shame_2_Z * as.factor(bias) + 
##     defensive_reverse_Z, data = wf_dt2)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.0405 -0.7375  0.1724  1.2527  2.7653 
## 
## Coefficients:
##                                 Estimate Std. Error t value            Pr(>|t|)
## (Intercept)                       4.5413     0.2213  20.523 <0.0000000000000002
## BMIS_shame_2_Z                   -0.2145     0.2328  -0.922               0.358
## as.factor(bias)1                  0.0216     0.3229   0.067               0.947
## defensive_reverse_Z              -0.2030     0.1591  -1.276               0.204
## BMIS_shame_2_Z:as.factor(bias)1   0.3573     0.3003   1.190               0.236
##                                    
## (Intercept)                     ***
## BMIS_shame_2_Z                     
## as.factor(bias)1                   
## defensive_reverse_Z                
## BMIS_shame_2_Z:as.factor(bias)1    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.717 on 133 degrees of freedom
##   (180 observations deleted due to missingness)
## Multiple R-squared:  0.02824,    Adjusted R-squared:  -0.0009891 
## F-statistic: 0.9662 on 4 and 133 DF,  p-value: 0.4284
summary(lm(shop_intentions_Z ~ BMIS_shame_2_Z*as.factor(brand_race) + as.factor(bias), wf_dt2))
## 
## Call:
## lm(formula = shop_intentions_Z ~ BMIS_shame_2_Z * as.factor(brand_race) + 
##     as.factor(bias), data = wf_dt2)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.90162 -0.38019  0.08682  0.63467  1.65863 
## 
## Coefficients:
##                                       Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                            0.01575    0.09122   0.173 0.863048    
## BMIS_shame_2_Z                        -0.09352    0.07529  -1.242 0.215190    
## as.factor(brand_race)1                 0.32940    0.09898   3.328 0.000992 ***
## as.factor(bias)1                       0.02334    0.10310   0.226 0.821031    
## BMIS_shame_2_Z:as.factor(brand_race)1  0.07908    0.09967   0.793 0.428235    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8328 on 280 degrees of freedom
##   (33 observations deleted due to missingness)
## Multiple R-squared:  0.04186,    Adjusted R-squared:  0.02818 
## F-statistic: 3.059 on 4 and 280 DF,  p-value: 0.01722
summary(lm(shop_intentions_Z ~ BMIS_shame_2_Z*as.factor(brand_race), wf_dt2))
## 
## Call:
## lm(formula = shop_intentions_Z ~ BMIS_shame_2_Z * as.factor(brand_race), 
##     data = wf_dt2)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.88813 -0.39432  0.07269  0.64785  1.65929 
## 
## Coefficients:
##                                       Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                            0.02892    0.07016   0.412 0.680542    
## BMIS_shame_2_Z                        -0.08940    0.07292  -1.226 0.221270    
## as.factor(brand_race)1                 0.32825    0.09868   3.326 0.000997 ***
## BMIS_shame_2_Z:as.factor(brand_race)1  0.07725    0.09918   0.779 0.436695    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8314 on 281 degrees of freedom
##   (33 observations deleted due to missingness)
## Multiple R-squared:  0.04169,    Adjusted R-squared:  0.03146 
## F-statistic: 4.075 on 3 and 281 DF,  p-value: 0.007428
summary(lm(shop_intentions_Z ~ BMIS_shame_2_Z*as.factor(bias) + defensive_reverse_Z, wf_dt2))
## 
## Call:
## lm(formula = shop_intentions_Z ~ BMIS_shame_2_Z * as.factor(bias) + 
##     defensive_reverse_Z, data = wf_dt2)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.8724 -0.4427  0.1191  0.5428  1.5247 
## 
## Coefficients:
##                                 Estimate Std. Error t value Pr(>|t|)   
## (Intercept)                      0.11380    0.07993   1.424  0.15564   
## BMIS_shame_2_Z                  -0.14464    0.09406  -1.538  0.12528   
## as.factor(bias)1                 0.11728    0.10967   1.069  0.28585   
## defensive_reverse_Z             -0.15344    0.05310  -2.890  0.00417 **
## BMIS_shame_2_Z:as.factor(bias)1  0.14429    0.11374   1.269  0.20566   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8351 on 272 degrees of freedom
##   (41 observations deleted due to missingness)
## Multiple R-squared:  0.04132,    Adjusted R-squared:  0.02723 
## F-statistic: 2.931 on 4 and 272 DF,  p-value: 0.02129
summary(lm(rl_shop_intentions ~ BMIS_shame_2_Z*as.factor(bias), wf_dt2))
## 
## Call:
## lm(formula = rl_shop_intentions ~ BMIS_shame_2_Z * as.factor(bias), 
##     data = wf_dt2)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.1795 -1.2096 -0.1443  1.1149  3.2445 
## 
## Coefficients:
##                                 Estimate Std. Error t value            Pr(>|t|)
## (Intercept)                       3.4274     0.2812  12.188 <0.0000000000000002
## BMIS_shame_2_Z                   -0.9345     0.3849  -2.428              0.0165
## as.factor(bias)1                  0.6164     0.3521   1.751              0.0823
## BMIS_shame_2_Z:as.factor(bias)1   0.8097     0.4291   1.887              0.0613
##                                    
## (Intercept)                     ***
## BMIS_shame_2_Z                  *  
## as.factor(bias)1                .  
## BMIS_shame_2_Z:as.factor(bias)1 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.813 on 136 degrees of freedom
##   (178 observations deleted due to missingness)
## Multiple R-squared:  0.04694,    Adjusted R-squared:  0.02592 
## F-statistic: 2.233 on 3 and 136 DF,  p-value: 0.08721
summary(lm(rr_shop_intentions ~ BMIS_shame_2_Z + as.factor(bias), wf_dt2))
## 
## Call:
## lm(formula = rr_shop_intentions ~ BMIS_shame_2_Z + as.factor(bias), 
##     data = wf_dt2)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.6871 -0.6776  0.3129  1.3153  2.5122 
## 
## Coefficients:
##                   Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)       4.679702   0.206417  22.671 <0.0000000000000002 ***
## BMIS_shame_2_Z   -0.009222   0.140755  -0.066               0.948    
## as.factor(bias)1 -0.170631   0.290400  -0.588               0.558    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.705 on 141 degrees of freedom
##   (174 observations deleted due to missingness)
## Multiple R-squared:  0.002694,   Adjusted R-squared:  -0.01145 
## F-statistic: 0.1904 on 2 and 141 DF,  p-value: 0.8268
summary(lm(rl_shop_intentions ~ BMIS_shame_2_Z + as.factor(bias), wf_dt2))
## 
## Call:
## lm(formula = rl_shop_intentions ~ BMIS_shame_2_Z + as.factor(bias), 
##     data = wf_dt2)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.3104 -1.4297  0.0674  1.2768  3.5703 
## 
## Coefficients:
##                  Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)        3.7049     0.2419  15.315 <0.0000000000000002 ***
## BMIS_shame_2_Z    -0.2828     0.1716  -1.648               0.102    
## as.factor(bias)1   0.3778     0.3317   1.139               0.257    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.83 on 137 degrees of freedom
##   (178 observations deleted due to missingness)
## Multiple R-squared:  0.02199,    Adjusted R-squared:  0.007711 
## F-statistic:  1.54 on 2 and 137 DF,  p-value: 0.2181
summary(lm(rr_shop_intentions ~ BMIS_shame_2_Z, wf_dt2))
## 
## Call:
## lm(formula = rr_shop_intentions ~ BMIS_shame_2_Z, data = wf_dt2)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.6128 -0.6128  0.3872  1.3872  2.4682 
## 
## Coefficients:
##                Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)     4.59184    0.14197  32.345 <0.0000000000000002 ***
## BMIS_shame_2_Z -0.02601    0.13751  -0.189                0.85    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.701 on 142 degrees of freedom
##   (174 observations deleted due to missingness)
## Multiple R-squared:  0.0002519,  Adjusted R-squared:  -0.006789 
## F-statistic: 0.03578 on 1 and 142 DF,  p-value: 0.8502
summary(lm(rl_shop_intentions ~ BMIS_shame_2_Z, wf_dt2))
## 
## Call:
## lm(formula = rl_shop_intentions ~ BMIS_shame_2_Z, data = wf_dt2)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.0894 -1.4763 -0.0894  1.1335  3.5795 
## 
## Coefficients:
##                Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)      3.9165     0.1551  25.248 <0.0000000000000002 ***
## BMIS_shame_2_Z  -0.2148     0.1611  -1.334               0.184    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.832 on 138 degrees of freedom
##   (178 observations deleted due to missingness)
## Multiple R-squared:  0.01273,    Adjusted R-squared:  0.005572 
## F-statistic: 1.779 on 1 and 138 DF,  p-value: 0.1845

Shame does not seem to be related to purchase behavior at all.

summary(lm(rr_shop_intentions ~ shame_discrepancy + as.factor(bias) + defensive_reverse_Z, wf_dt2))
## 
## Call:
## lm(formula = rr_shop_intentions ~ shame_discrepancy + as.factor(bias) + 
##     defensive_reverse_Z, data = wf_dt2)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.2200 -0.6932  0.1736  1.2476  2.9549 
## 
## Coefficients:
##                     Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)           4.6645     0.2251  20.723 <0.0000000000000002 ***
## shame_discrepancy     0.3395     0.1974   1.720              0.0878 .  
## as.factor(bias)1     -0.1153     0.3275  -0.352              0.7252    
## defensive_reverse_Z  -0.2396     0.1552  -1.544              0.1250    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.701 on 134 degrees of freedom
##   (180 observations deleted due to missingness)
## Multiple R-squared:  0.03909,    Adjusted R-squared:  0.01758 
## F-statistic: 1.817 on 3 and 134 DF,  p-value: 0.147
summary(lm(rr_shop_intentions ~ shame_discrepancy*as.factor(bias) + defensive_reverse_Z, wf_dt2))
## 
## Call:
## lm(formula = rr_shop_intentions ~ shame_discrepancy * as.factor(bias) + 
##     defensive_reverse_Z, data = wf_dt2)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.3355 -0.7036  0.1668  1.3122  2.9816 
## 
## Coefficients:
##                                    Estimate Std. Error t value
## (Intercept)                         4.61642    0.24409  18.913
## shame_discrepancy                   0.17915    0.36808   0.487
## as.factor(bias)1                   -0.08099    0.33507  -0.242
## defensive_reverse_Z                -0.23858    0.15566  -1.533
## shame_discrepancy:as.factor(bias)1  0.22564    0.43660   0.517
##                                               Pr(>|t|)    
## (Intercept)                        <0.0000000000000002 ***
## shame_discrepancy                                0.627    
## as.factor(bias)1                                 0.809    
## defensive_reverse_Z                              0.128    
## shame_discrepancy:as.factor(bias)1               0.606    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.706 on 133 degrees of freedom
##   (180 observations deleted due to missingness)
## Multiple R-squared:  0.04102,    Adjusted R-squared:  0.01218 
## F-statistic: 1.422 on 4 and 133 DF,  p-value: 0.23
summary(lm(rr_shop_intentions ~ shame_discrepancy*as.factor(bias), wf_dt2))
## 
## Call:
## lm(formula = rr_shop_intentions ~ shame_discrepancy * as.factor(bias), 
##     data = wf_dt2)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.2412 -0.7069  0.2929  1.2931  2.6647 
## 
## Coefficients:
##                                    Estimate Std. Error t value
## (Intercept)                          4.7193     0.2282  20.678
## shame_discrepancy                    0.1284     0.3475   0.369
## as.factor(bias)1                    -0.2905     0.3036  -0.957
## shame_discrepancy:as.factor(bias)1   0.2820     0.4179   0.675
##                                               Pr(>|t|)    
## (Intercept)                        <0.0000000000000002 ***
## shame_discrepancy                                0.712    
## as.factor(bias)1                                 0.340    
## shame_discrepancy:as.factor(bias)1               0.501    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.691 on 140 degrees of freedom
##   (174 observations deleted due to missingness)
## Multiple R-squared:  0.02538,    Adjusted R-squared:  0.004496 
## F-statistic: 1.215 on 3 and 140 DF,  p-value: 0.3065
summary(lm(shop_intentions_Z ~ shame_discrepancy*as.factor(brand_race) + as.factor(bias), wf_dt2))
## 
## Call:
## lm(formula = shop_intentions_Z ~ shame_discrepancy * as.factor(brand_race) + 
##     as.factor(bias), data = wf_dt2)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.9118 -0.4506  0.1116  0.6016  1.5356 
## 
## Coefficients:
##                                          Estimate Std. Error t value Pr(>|t|)
## (Intercept)                               0.08118    0.09017   0.900 0.368725
## shame_discrepancy                         0.17501    0.09376   1.867 0.062997
## as.factor(brand_race)1                    0.33084    0.09814   3.371 0.000854
## as.factor(bias)1                         -0.09516    0.10490  -0.907 0.365096
## shame_discrepancy:as.factor(brand_race)1 -0.03625    0.12717  -0.285 0.775803
##                                             
## (Intercept)                                 
## shame_discrepancy                        .  
## as.factor(brand_race)1                   ***
## as.factor(bias)1                            
## shame_discrepancy:as.factor(brand_race)1    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8272 on 280 degrees of freedom
##   (33 observations deleted due to missingness)
## Multiple R-squared:  0.05477,    Adjusted R-squared:  0.04127 
## F-statistic: 4.056 on 4 and 280 DF,  p-value: 0.003258
summary(lm(rl_shop_intentions ~ shame_discrepancy*as.factor(bias), wf_dt2))
## 
## Call:
## lm(formula = rl_shop_intentions ~ shame_discrepancy * as.factor(bias), 
##     data = wf_dt2)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.2109 -1.8066  0.1516  1.1934  3.1934 
## 
## Coefficients:
##                                    Estimate Std. Error t value
## (Intercept)                         3.85973    0.26336  14.656
## shame_discrepancy                   0.11730    0.42810   0.274
## as.factor(bias)1                    0.03559    0.34502   0.103
## shame_discrepancy:as.factor(bias)1  0.27216    0.49656   0.548
##                                               Pr(>|t|)    
## (Intercept)                        <0.0000000000000002 ***
## shame_discrepancy                                0.784    
## as.factor(bias)1                                 0.918    
## shame_discrepancy:as.factor(bias)1               0.585    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.839 on 136 degrees of freedom
##   (178 observations deleted due to missingness)
## Multiple R-squared:  0.0204, Adjusted R-squared:  -0.001211 
## F-statistic: 0.944 on 3 and 136 DF,  p-value: 0.4214
summary(lm(rr_shop_intentions ~ shame_discrepancy + as.factor(bias), wf_dt2))
## 
## Call:
## lm(formula = rr_shop_intentions ~ shame_discrepancy + as.factor(bias), 
##     data = wf_dt2)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -4.085 -0.746  0.254  1.254  3.010 
## 
## Coefficients:
##                   Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)         4.7771     0.2111  22.631 <0.0000000000000002 ***
## shame_discrepancy   0.3234     0.1926   1.679              0.0954 .  
## as.factor(bias)1   -0.3318     0.2967  -1.118              0.2654    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.688 on 141 degrees of freedom
##   (174 observations deleted due to missingness)
## Multiple R-squared:  0.02221,    Adjusted R-squared:  0.008341 
## F-statistic: 1.601 on 2 and 141 DF,  p-value: 0.2053
summary(lm(rl_shop_intentions ~ shame_discrepancy + as.factor(bias), wf_dt2))
## 
## Call:
## lm(formula = rl_shop_intentions ~ shame_discrepancy + as.factor(bias), 
##     data = wf_dt2)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.1754 -1.8140  0.1143  1.1563  3.1563 
## 
## Coefficients:
##                    Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)        3.918950   0.239566  16.359 <0.0000000000000002 ***
## shame_discrepancy  0.319589   0.216358   1.477               0.142    
## as.factor(bias)1  -0.002518   0.337075  -0.007               0.994    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.834 on 137 degrees of freedom
##   (178 observations deleted due to missingness)
## Multiple R-squared:  0.01823,    Adjusted R-squared:  0.003902 
## F-statistic: 1.272 on 2 and 137 DF,  p-value: 0.2835
summary(lm(rr_shop_intentions ~ shame_discrepancy, wf_dt2))
## 
## Call:
## lm(formula = rr_shop_intentions ~ shame_discrepancy, data = wf_dt2)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.1069 -0.5768  0.3243  1.4232  3.0204 
## 
## Coefficients:
##                   Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)         4.6014     0.1410  32.633 <0.0000000000000002 ***
## shame_discrepancy   0.2554     0.1829   1.396               0.165    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.689 on 142 degrees of freedom
##   (174 observations deleted due to missingness)
## Multiple R-squared:  0.01354,    Adjusted R-squared:  0.006591 
## F-statistic: 1.949 on 1 and 142 DF,  p-value: 0.1649
summary(lm(rl_shop_intentions ~ shame_discrepancy, wf_dt2))
## 
## Call:
## lm(formula = rl_shop_intentions ~ shame_discrepancy, data = wf_dt2)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.1760 -1.8135  0.1131  1.1550  3.1550 
## 
## Coefficients:
##                   Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)         3.9176     0.1546  25.344 <0.0000000000000002 ***
## shame_discrepancy   0.3190     0.1992   1.601               0.112    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.827 on 138 degrees of freedom
##   (178 observations deleted due to missingness)
## Multiple R-squared:  0.01823,    Adjusted R-squared:  0.01112 
## F-statistic: 2.563 on 1 and 138 DF,  p-value: 0.1117
summary(lm(rr_shop_intentions ~ bias_aware_Z + as.factor(bias), wf_dt2))
## 
## Call:
## lm(formula = rr_shop_intentions ~ bias_aware_Z + as.factor(bias), 
##     data = wf_dt2)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.6975 -0.6707  0.3025  1.3182  2.5382 
## 
## Coefficients:
##                  Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)       4.68522    0.20652  22.687 <0.0000000000000002 ***
## bias_aware_Z      0.02618    0.15059   0.174               0.862    
## as.factor(bias)1 -0.17753    0.28486  -0.623               0.534    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.704 on 141 degrees of freedom
##   (174 observations deleted due to missingness)
## Multiple R-squared:  0.002877,   Adjusted R-squared:  -0.01127 
## F-statistic: 0.2034 on 2 and 141 DF,  p-value: 0.8162
summary(lm(rr_shop_intentions ~ bias_aware_Z*as.factor(bias), wf_dt2))
## 
## Call:
## lm(formula = rr_shop_intentions ~ bias_aware_Z * as.factor(bias), 
##     data = wf_dt2)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.8125 -0.6776  0.1875  1.2953  2.6549 
## 
## Coefficients:
##                               Estimate Std. Error t value            Pr(>|t|)
## (Intercept)                     4.7138     0.2081  22.650 <0.0000000000000002
## bias_aware_Z                    0.2105     0.2282   0.922               0.358
## as.factor(bias)1               -0.2116     0.2865  -0.739               0.461
## bias_aware_Z:as.factor(bias)1  -0.3261     0.3036  -1.074               0.285
##                                  
## (Intercept)                   ***
## bias_aware_Z                     
## as.factor(bias)1                 
## bias_aware_Z:as.factor(bias)1    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.704 on 140 degrees of freedom
##   (174 observations deleted due to missingness)
## Multiple R-squared:  0.01103,    Adjusted R-squared:  -0.01017 
## F-statistic: 0.5203 on 3 and 140 DF,  p-value: 0.669
summary(lm(shop_intentions_Z ~ bias_aware_Z*as.factor(brand_race)*as.factor(bias), wf_dt2))
## 
## Call:
## lm(formula = shop_intentions_Z ~ bias_aware_Z * as.factor(brand_race) * 
##     as.factor(bias), data = wf_dt2)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2.07569 -0.65587  0.09075  0.72615  1.68549 
## 
## Coefficients:
##                                                      Estimate Std. Error
## (Intercept)                                          -0.14456    0.11166
## bias_aware_Z                                          0.06588    0.10967
## as.factor(brand_race)1                                0.39754    0.15626
## as.factor(bias)1                                      0.16122    0.15293
## bias_aware_Z:as.factor(brand_race)1                  -0.05128    0.16363
## bias_aware_Z:as.factor(bias)1                        -0.06703    0.14614
## as.factor(brand_race)1:as.factor(bias)1              -0.17950    0.21560
## bias_aware_Z:as.factor(brand_race)1:as.factor(bias)1  0.01432    0.21846
##                                                      t value Pr(>|t|)  
## (Intercept)                                           -1.295   0.1964  
## bias_aware_Z                                           0.601   0.5485  
## as.factor(brand_race)1                                 2.544   0.0115 *
## as.factor(bias)1                                       1.054   0.2927  
## bias_aware_Z:as.factor(brand_race)1                   -0.313   0.7542  
## bias_aware_Z:as.factor(bias)1                         -0.459   0.6468  
## as.factor(brand_race)1:as.factor(bias)1               -0.833   0.4058  
## bias_aware_Z:as.factor(brand_race)1:as.factor(bias)1   0.066   0.9478  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9314 on 294 degrees of freedom
##   (16 observations deleted due to missingness)
## Multiple R-squared:  0.03092,    Adjusted R-squared:  0.007845 
## F-statistic:  1.34 on 7 and 294 DF,  p-value: 0.2311
summary(lm(rl_shop_intentions ~ bias_aware_Z*as.factor(bias), wf_dt2))
## 
## Call:
## lm(formula = rl_shop_intentions ~ bias_aware_Z * as.factor(bias), 
##     data = wf_dt2)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.1311 -1.8420  0.1247  1.1378  3.2090 
## 
## Coefficients:
##                                Estimate Std. Error t value            Pr(>|t|)
## (Intercept)                    3.860410   0.233647  16.522 <0.0000000000000002
## bias_aware_Z                  -0.008493   0.228456  -0.037               0.970
## as.factor(bias)1               0.164364   0.316026   0.520               0.604
## bias_aware_Z:as.factor(bias)1 -0.091074   0.302654  -0.301               0.764
##                                  
## (Intercept)                   ***
## bias_aware_Z                     
## as.factor(bias)1                 
## bias_aware_Z:as.factor(bias)1    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.856 on 137 degrees of freedom
##   (177 observations deleted due to missingness)
## Multiple R-squared:  0.003581,   Adjusted R-squared:  -0.01824 
## F-statistic: 0.1641 on 3 and 137 DF,  p-value: 0.9204
summary(lm(rr_shop_intentions ~ bias_aware_Z + as.factor(bias), wf_dt2))
## 
## Call:
## lm(formula = rr_shop_intentions ~ bias_aware_Z + as.factor(bias), 
##     data = wf_dt2)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.6975 -0.6707  0.3025  1.3182  2.5382 
## 
## Coefficients:
##                  Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)       4.68522    0.20652  22.687 <0.0000000000000002 ***
## bias_aware_Z      0.02618    0.15059   0.174               0.862    
## as.factor(bias)1 -0.17753    0.28486  -0.623               0.534    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.704 on 141 degrees of freedom
##   (174 observations deleted due to missingness)
## Multiple R-squared:  0.002877,   Adjusted R-squared:  -0.01127 
## F-statistic: 0.2034 on 2 and 141 DF,  p-value: 0.8162
summary(lm(rl_shop_intentions ~ bias_aware_Z + as.factor(bias), wf_dt2))
## 
## Call:
## lm(formula = rl_shop_intentions ~ bias_aware_Z + as.factor(bias), 
##     data = wf_dt2)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -3.08465 -1.79717  0.04943  1.12032  3.12163 
## 
## Coefficients:
##                  Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)       3.86674    0.23193  16.672 <0.0000000000000002 ***
## bias_aware_Z     -0.06039    0.14935  -0.404               0.687    
## as.factor(bias)1  0.15340    0.31288   0.490               0.625    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.85 on 138 degrees of freedom
##   (177 observations deleted due to missingness)
## Multiple R-squared:  0.002923,   Adjusted R-squared:  -0.01153 
## F-statistic: 0.2023 on 2 and 138 DF,  p-value: 0.8171
summary(lm(rr_shop_intentions ~ bias_aware_Z, wf_dt2))
## 
## Call:
## lm(formula = rr_shop_intentions ~ bias_aware_Z, data = wf_dt2)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.6157 -0.6053  0.3930  1.4017  2.4436 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)   4.59221    0.14244  32.239 <0.0000000000000002 ***
## bias_aware_Z  0.02043    0.14998   0.136               0.892    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.701 on 142 degrees of freedom
##   (174 observations deleted due to missingness)
## Multiple R-squared:  0.0001306,  Adjusted R-squared:  -0.006911 
## F-statistic: 0.01855 on 1 and 142 DF,  p-value: 0.8918
summary(lm(rl_shop_intentions ~ bias_aware_Z, wf_dt2))
## 
## Call:
## lm(formula = rl_shop_intentions ~ bias_aware_Z, data = wf_dt2)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.0255 -1.8395  0.0675  1.0985  3.1915 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)   3.95052    0.15637  25.263 <0.0000000000000002 ***
## bias_aware_Z -0.06051    0.14894  -0.406               0.685    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.845 on 139 degrees of freedom
##   (177 observations deleted due to missingness)
## Multiple R-squared:  0.001186,   Adjusted R-squared:  -0.006 
## F-statistic: 0.165 on 1 and 139 DF,  p-value: 0.6852

7. Word of Mouth

summary(lm(wom_Z ~ as.factor(bias)*as.factor(brand_race), wf_dt2)) # main interaction working, it is defensiveness driving this effect, whe defensiveness is included as a term in this model the effect vanishes, but defensiveness on its own is still negatively correlated with the dv, it also loses its sig for shame and se, but they are not signifgicant predictors of the dv
## 
## Call:
## lm(formula = wom_Z ~ as.factor(bias) * as.factor(brand_race), 
##     data = wf_dt2)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.0582 -0.7747  0.1550  0.7199  2.0588 
## 
## Coefficients:
##                                         Estimate Std. Error t value    Pr(>|t|)
## (Intercept)                              -0.4693     0.1024  -4.584 0.000006596
## as.factor(bias)1                          0.3870     0.1475   2.624     0.00911
## as.factor(brand_race)1                    0.7853     0.1505   5.217 0.000000331
## as.factor(bias)1:as.factor(brand_race)1  -0.4010     0.2133  -1.880     0.06099
##                                            
## (Intercept)                             ***
## as.factor(bias)1                        ** 
## as.factor(brand_race)1                  ***
## as.factor(bias)1:as.factor(brand_race)1 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9494 on 314 degrees of freedom
## Multiple R-squared:  0.1072, Adjusted R-squared:  0.09868 
## F-statistic: 12.57 on 3 and 314 DF,  p-value: 0.00000008821
summary(lm(wom_Z ~ shame_discrepancy, wf_dt2)) # shame does not predict it neither does shame change, and it doesn't interact with anything to predict it
## 
## Call:
## lm(formula = wom_Z ~ shame_discrepancy, data = wf_dt2)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.66782 -0.49641 -0.02045  0.46941  1.65472 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)        0.19227    0.05057   3.802 0.000176 ***
## shame_discrepancy  0.10573    0.06551   1.614 0.107629    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8537 on 283 degrees of freedom
##   (33 observations deleted due to missingness)
## Multiple R-squared:  0.009122,   Adjusted R-squared:  0.00562 
## F-statistic: 2.605 on 1 and 283 DF,  p-value: 0.1076
summary(lm(wom_Z ~ defensive_reverse_Z*as.factor(brand_race), wf_dt2)) # defensiveness does predict it
## 
## Call:
## lm(formula = wom_Z ~ defensive_reverse_Z * as.factor(brand_race), 
##     data = wf_dt2)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.92602 -0.42021  0.08609  0.53949  1.75657 
## 
## Coefficients:
##                                            Estimate Std. Error t value
## (Intercept)                                -0.03836    0.06931  -0.553
## defensive_reverse_Z                        -0.15842    0.06996  -2.264
## as.factor(brand_race)1                      0.46415    0.09837   4.718
## defensive_reverse_Z:as.factor(brand_race)1  0.04662    0.09855   0.473
##                                             Pr(>|t|)    
## (Intercept)                                   0.5804    
## defensive_reverse_Z                           0.0243 *  
## as.factor(brand_race)1                     0.0000038 ***
## defensive_reverse_Z:as.factor(brand_race)1    0.6365    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.82 on 274 degrees of freedom
##   (40 observations deleted due to missingness)
## Multiple R-squared:  0.09728,    Adjusted R-squared:  0.0874 
## F-statistic: 9.843 on 3 and 274 DF,  p-value: 0.000003472
summary(lm(wom_Z ~ SE_change, wf_dt2)) # self-esteem change does not
## 
## Call:
## lm(formula = wom_Z ~ SE_change, data = wf_dt2)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.4919 -0.5102 -0.0340  0.4420  1.4087 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.19504    0.05073   3.844 0.000149 ***
## SE_change   -0.01925    0.16637  -0.116 0.907976    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8578 on 284 degrees of freedom
##   (32 observations deleted due to missingness)
## Multiple R-squared:  4.713e-05,  Adjusted R-squared:  -0.003474 
## F-statistic: 0.01339 on 1 and 284 DF,  p-value: 0.908
summary(lm(wom_Z ~ bias_discrepancy, wf_dt2)) # BD does not
## 
## Call:
## lm(formula = wom_Z ~ bias_discrepancy, data = wf_dt2)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.52951 -0.50224 -0.02628  0.44968  1.49203 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)       0.18792    0.05224   3.597 0.000379 ***
## bias_discrepancy -0.01507    0.02710  -0.556 0.578549    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8574 on 284 degrees of freedom
##   (32 observations deleted due to missingness)
## Multiple R-squared:  0.001088,   Adjusted R-squared:  -0.002429 
## F-statistic: 0.3093 on 1 and 284 DF,  p-value: 0.5785
summary(lm(wom_Z ~ bias_aware_Z*as.factor(brand_race)*as.factor(bias), wf_dt2)) # BA does not
## 
## Call:
## lm(formula = wom_Z ~ bias_aware_Z * as.factor(brand_race) * as.factor(bias), 
##     data = wf_dt2)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.1145 -0.6867  0.2219  0.7105  1.7909 
## 
## Coefficients:
##                                                      Estimate Std. Error
## (Intercept)                                          -0.18449    0.11062
## bias_aware_Z                                          0.07858    0.10866
## as.factor(brand_race)1                                0.49146    0.15482
## as.factor(bias)1                                      0.09996    0.15152
## bias_aware_Z:as.factor(brand_race)1                  -0.15133    0.16211
## bias_aware_Z:as.factor(bias)1                        -0.05385    0.14479
## as.factor(brand_race)1:as.factor(bias)1              -0.10521    0.21361
## bias_aware_Z:as.factor(brand_race)1:as.factor(bias)1  0.12039    0.21643
##                                                      t value Pr(>|t|)   
## (Intercept)                                           -1.668  0.09643 . 
## bias_aware_Z                                           0.723  0.47015   
## as.factor(brand_race)1                                 3.174  0.00166 **
## as.factor(bias)1                                       0.660  0.50995   
## bias_aware_Z:as.factor(brand_race)1                   -0.934  0.35133   
## bias_aware_Z:as.factor(bias)1                         -0.372  0.71023   
## as.factor(brand_race)1:as.factor(bias)1               -0.493  0.62270   
## bias_aware_Z:as.factor(brand_race)1:as.factor(bias)1   0.556  0.57847   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9228 on 294 degrees of freedom
##   (16 observations deleted due to missingness)
## Multiple R-squared:  0.05836,    Adjusted R-squared:  0.03594 
## F-statistic: 2.603 on 7 and 294 DF,  p-value: 0.01278

*** Correlation Matrices ****

colnames(wf_dt2)
##   [1] "ParticipantID"              "StartDate"                 
##   [3] "EndDate"                    "Status"                    
##   [5] "Progress"                   "Duration (in seconds)"     
##   [7] "Finished"                   "RecordedDate"              
##   [9] "ResponseId"                 "DistributionChannel"       
##  [11] "UserLanguage"               "consent"                   
##  [13] "prolificID"                 "BA_1"                      
##  [15] "BA_2"                       "BA_3"                      
##  [17] "BA_4"                       "SE1"                       
##  [19] "BMIS_sad_1"                 "BMIS_shame_1"              
##  [21] "BMIS_guilt_1"               "BMIS_tired_1"              
##  [23] "BMIS_nervous_1"             "BMIS_calm_1"               
##  [25] "BMIS_fedup_1"               "BMIS_loving_1"             
##  [27] "BMIS_angry_1"               "BMIS_lively_1"             
##  [29] "BMIS_caring_1"              "BMIS_content_1"            
##  [31] "BMIS_gloomy_1"              "BMIS_jittery_1"            
##  [33] "BMIS_drowsy_1"              "BMIS_happy_1"              
##  [35] "Q1 RP1"                     "Q2 RP2"                    
##  [37] "Q3 RP3"                     "Q4 RP4"                    
##  [39] "Q5 RP5"                     "Q6 RP6"                    
##  [41] "Q7 RP7"                     "Q8 RN1"                    
##  [43] "Q9 RN2"                     "Q10 RN3"                   
##  [45] "Q11 RN4"                    "Q12 RN5"                   
##  [47] "Q13 RN6"                    "Q14 RN7"                   
##  [49] "Q15 LP1"                    "Q16 LP2"                   
##  [51] "Q17 LP3"                    "Q18 LP4"                   
##  [53] "Q19 LP5"                    "Q20 LP6"                   
##  [55] "Q21 LP7"                    "Q22 LN1"                   
##  [57] "Q23 LN2"                    "Q24 LN3"                   
##  [59] "Q25 LN4"                    "Q26 LN5"                   
##  [61] "Q27 LN6"                    "Q28 LN7"                   
##  [63] "spwtime_First Click"        "spwtime_Last Click"        
##  [65] "spwtime_Page Submit"        "spwtime_Click Count"       
##  [67] "attentioncheck_nb"          "discrepancy_nb"            
##  [69] "BMIS_sad_2"                 "BMIS_shame_2"              
##  [71] "BMIS_guilt_2"               "BMIS_tired_2"              
##  [73] "BMIS_nervous_2"             "BMIS_calm_2"               
##  [75] "BMIS_fedup_2"               "BMIS_loving_2"             
##  [77] "BMIS_angry_2"               "BMIS_lively_2"             
##  [79] "BMIS_caring_2"              "BMIS_content_2"            
##  [81] "BMIS_gloomy_2"              "BMIS_jittery_2"            
##  [83] "BMIS_drowsy_2"              "BMIS_happy_2"              
##  [85] "credibility"                "objective"                 
##  [87] "valid"                      "useful"                    
##  [89] "rl_product_choice"          "rl_shop_intentions"        
##  [91] "rl_purchase"                "rl_wom"                    
##  [93] "rr_product_choice"          "rr_shop_intentions"        
##  [95] "rr_purchase"                "rr_wom"                    
##  [97] "attentioncheck_b"           "discrepancy_b"             
##  [99] "SE_2"                       "age"                       
## [101] "race"                       "education"                 
## [103] "polit_affil"                "polit_affil_4_TEXT"        
## [105] "polit_affil_cont_1"         "gender"                    
## [107] "gender_4_TEXT"              "iat_prev"                  
## [109] "iat_racial"                 "iat_racial_time"           
## [111] "iat_racial_quant"           "recent_results"            
## [113] "nobias_white"               "nobias_black"              
## [115] "bias_white"                 "bias_black"                
## [117] "discrepancy_bias"           "discrepancy_nobias"        
## [119] "bias_discrepancy"           "bias_discrepancy_centered" 
## [121] "bias_discrepancy_Z"         "condition"                 
## [123] "bias"                       "brand_race"                
## [125] "shop_intentions"            "shop_intentions_centered"  
## [127] "shop_intentions_Z"          "purchase"                  
## [129] "purchase_centered"          "purchase_Z"                
## [131] "wom"                        "wom_centered"              
## [133] "wom_Z"                      "defensive"                 
## [135] "defensive_reverse"          "defensive_reverse_centered"
## [137] "defensive_reverse_Z"        "bias_aware"                
## [139] "bias_aware_center"          "bias_aware_Z"              
## [141] "BMIS_guilt_1_center"        "BMIS_guilt_1_Z"            
## [143] "BMIS_shame_1_center"        "BMIS_shame_1_Z"            
## [145] "BMIS_sad_1_center"          "BMIS_sad_1_Z"              
## [147] "BMIS_guilt_2_center"        "BMIS_guilt_2_Z"            
## [149] "BMIS_shame_2_center"        "BMIS_shame_2_Z"            
## [151] "BMIS_sad_2_center"          "BMIS_sad_2_Z"              
## [153] "guilt_shame_sad"            "guilt_shame_sad_center"    
## [155] "guilt_shame_sad_Z"          "shame_discrepancy"         
## [157] "SE_2_Z"                     "SE_1_Z"                    
## [159] "SE_change"
# Calculate correlation between variables
cor_matrix <- wf_dt2 %>%
select(
  "BMIS_shame_2_Z",
  "defensive_reverse_Z",
  "bias_aware_Z",
  "guilt_shame_sad_Z",
  "SE_2_Z",
  "shop_intentions",
  "wom",
  "purchase",
  "polit_affil_cont_1",
  "rl_shop_intentions",
  "rl_purchase",
  "rl_wom",
  "rr_shop_intentions",
  "rr_purchase",
  "rr_wom") %>%
  cor(use = "pairwise.complete.obs")

# Create a function to calculate p-values
get_p_value <- function(x, y) {
  cor_test <- cor.test(x, y, method = "pearson")
  return(cor_test$p.value)
}

# Calculate p-values for the correlations
p_values <- outer(colnames(cor_matrix), colnames(cor_matrix), 
                   Vectorize(function(x, y) get_p_value(cor_matrix[, x], cor_matrix[, y])))

# Create a dataframe for the correlation values and p-values
corr_data <- data.frame(variables = rep(colnames(cor_matrix), each = ncol(cor_matrix)),
                        others = rep(colnames(cor_matrix), ncol(cor_matrix)),
                        corr_values = as.vector(cor_matrix),
                        p_value = as.vector(p_values))

# Create a function to add asterisks based on p-values
add_asterisk <- function(p_value) {
  if (p_value < 0.05) {
    return("*")
  } else {
    return("")
  }
}

# Apply the function to generate the asterisk labels
corr_data$asterisks <- sapply(corr_data$p_value, add_asterisk)

# Create a heatmap for the correlation matrix with labels and significance asterisks
heatmap_plot <- ggplot(data = corr_data, aes(x = variables, y = others, fill = corr_values)) +
  geom_tile() +
  geom_text(aes(label = sprintf("%.2f", corr_values)), color = "black", size = 3) +  # Display correlation values as labels
  geom_text(aes(label = asterisks), color = "red", size = 5) +  # Display significance asterisks
  scale_fill_gradient2(low = "blue", mid = "white", high = "red", 
                       midpoint = 0, limits = c(-1, 1)) +
  labs(title = "Correlation Heatmap",
       x = "Variables",
       y = "Other Variables") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

# Display the heatmap
print(heatmap_plot)

# Install and load the writexl package if not already installed
# install.packages("writexl")
library(writexl)

# Specify the file path where you want to save the Excel file
excel_file_path <- "correlation_matrix.xlsx"

# Write the correlation matrix to an Excel sheet
write_xlsx(corr_data, path = excel_file_path)

# Display the file path to confirm where the Excel file is saved
excel_file_path
## [1] "correlation_matrix.xlsx"
old_data <- wf_dt2